{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rfu0mkFT7Fsp"
   },
   "source": [
    "Pytorch implementation of AAE \\\n",
    "A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, B. Frey, Adversarial Autoencoders, ICLR, 2018\\\n",
    "https://ai.google/research/pubs/pub44904"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.finfo(torch.float32).eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8CVQvgat-70F"
   },
   "outputs": [],
   "source": [
    "# based on https://github.com/shidilrzf/Adversarial-Autoencoders/blob/master/train.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "xt7lLIFvwRqt",
    "outputId": "1c82ef00-8163-4d8d-cb49-18e9bc0ed91f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "QJmNhIFu8Gom",
    "outputId": "1b64487f-d155-4c39-aecc-9570617c7bb8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "EPS = torch.finfo(torch.float32).eps\n",
    "\n",
    "\"\"\" GPU \"\"\"\n",
    "# Enable CUDA, set tensor type and device\n",
    "import torch\n",
    "\n",
    "use_cuda = True\n",
    "use_cuda = use_cuda and torch.cuda.is_available()\n",
    "print(use_cuda)\n",
    "if use_cuda:\n",
    "    dtype = torch.cuda.FloatTensor\n",
    "    device = torch.device('cuda:0')\n",
    "else:\n",
    "    dtype = torch.FloatTensor\n",
    "    device = torch.device('cpu')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "WDESDDo4wirW",
    "outputId": "5c799c12-c0b9-4094-b76a-6e2e9be7261f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘/content/drive/My Drive/aae’: File exists\n"
     ]
    }
   ],
   "source": [
    "!mkdir /content/drive/My\\ Drive/aae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "id": "fCO54wjJ88hb",
    "outputId": "f7ec0886-d36f-418f-a65e-d88685c029f7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from torch import autograd\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader, dataset\n",
    "from torchvision.datasets import MNIST\n",
    "import torchvision.transforms as T\n",
    "\n",
    "from tqdm.notebook import trange\n",
    "from torchsummary import summary\n",
    "import argparse\n",
    "import os\n",
    "import time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "joLHyjRt9IBg"
   },
   "outputs": [],
   "source": [
    "batch_size = 1024\n",
    "n_classes = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 667,
     "referenced_widgets": [
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      "6d52d8e228954fdd8dcc878ba4d11918",
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      "d139025d06a449a9b6054fdd8657d358",
      "fe86ea0ffd1146ddaff0ee1754457ae1"
     ]
    },
    "colab_type": "code",
    "id": "bYckim6I8WMu",
    "outputId": "3cdac951-0c7e-4b73-daf4-4681e1b6f709"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to Data/MNIST/raw/train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5055a419f76b4eeeb1631d10437c16d2",
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       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting Data/MNIST/raw/train-images-idx3-ubyte.gz to Data/MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to Data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab5d7fed15db410c9ed0b934573f81ee",
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       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting Data/MNIST/raw/train-labels-idx1-ubyte.gz to Data/MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to Data/MNIST/raw/t10k-images-idx3-ubyte.gz\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e370014a54d4cdc957e4ea9dc6d17a8",
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       "version_minor": 0
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       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting Data/MNIST/raw/t10k-images-idx3-ubyte.gz to Data/MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to Data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "18cdc20f99fb42f284806dfdd9771397",
       "version_major": 2,
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      },
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       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting Data/MNIST/raw/t10k-labels-idx1-ubyte.gz to Data/MNIST/raw\n",
      "Processing...\n",
      "Done!\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to Val/MNIST/raw/train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/pytorch/torch/csrc/utils/tensor_numpy.cpp:141: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program.\n"
     ]
    },
    {
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting Val/MNIST/raw/train-images-idx3-ubyte.gz to Val/MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to Val/MNIST/raw/train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54f61ee5814643fd80792202779689d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting Val/MNIST/raw/train-labels-idx1-ubyte.gz to Val/MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to Val/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fddd1b12997842db8562796bd1e773c9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting Val/MNIST/raw/t10k-images-idx3-ubyte.gz to Val/MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to Val/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "87856dac2b5a4df185f27383de4e9c5f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting Val/MNIST/raw/t10k-labels-idx1-ubyte.gz to Val/MNIST/raw\n",
      "Processing...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "train_loader = torch.utils.data.DataLoader(\n",
    "    MNIST(\n",
    "        'Data/',\n",
    "        train=True,\n",
    "        download=True,\n",
    "        transform=T.Compose([\n",
    "                T.transforms.ToTensor(),\n",
    "                T.Normalize((0.1307,), (0.3081,))  # mean and std deviation of the MNIST dataset\n",
    "        ])\n",
    "    ),\n",
    "    batch_size=batch_size,\n",
    "    shuffle=True\n",
    ")\n",
    "\n",
    "val_loader = torch.utils.data.DataLoader(\n",
    "    MNIST(\n",
    "        'Val/',\n",
    "        train=False,\n",
    "        download=True,\n",
    "        transform=T.Compose([\n",
    "                T.transforms.ToTensor(),\n",
    "                T.Normalize((0.1307,), (0.3081,))\n",
    "        ])\n",
    "    ),\n",
    "    batch_size=batch_size,\n",
    "    shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Sspvj-NI9ZxS"
   },
   "outputs": [],
   "source": [
    "supervised = True\n",
    "dims = 10  if not supervised else 2  # size of representation layer\n",
    "Model_dir = '/content/drive/My Drive/aae/'\n",
    "Fig_dir = '/content/drive/My Drive/aae/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QWzKYuqt8Csz"
   },
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "class Encoder(nn.Module):\n",
    "    '''Q-net'''\n",
    "    def __init__(self, dim_input, hidden_dim, dim_z):\n",
    "        super(Encoder, self).__init__()\n",
    "        self.dim_input = dim_input  # image size\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.dim_z = dim_z\n",
    "        self.network = []\n",
    "        self.network.extend([\n",
    "            nn.Linear(self.dim_input, self.hidden_dim),\n",
    "            nn.Dropout(p=0.2),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(self.hidden_dim, self.hidden_dim),\n",
    "            nn.Dropout(p=0.2),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(self.hidden_dim, self.dim_z),\n",
    "        ])\n",
    "        self.network = nn.Sequential(*self.network)\n",
    "    def forward(self, x):\n",
    "        z = self.network(x)\n",
    "        return z\n",
    "\n",
    "class Decoder(nn.Module):\n",
    "    '''P-net'''\n",
    "    def __init__(self, dim_input , hidden_dim, dim_z, supervised=False):\n",
    "        super(Decoder, self).__init__()\n",
    "        self.dim_input = dim_input\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.dim_z = dim_z\n",
    "        self.supervised = supervised\n",
    "        self.network = []\n",
    "        input_dim = self.dim_z if not self.supervised else self.dim_z + n_classes\n",
    "        self.network.extend([\n",
    "            nn.Linear(input_dim, self.hidden_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(p=0.2),\n",
    "            nn.Linear(self.hidden_dim, self.hidden_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(p=0.2),\n",
    "            nn.Linear(self.hidden_dim, self.dim_input),\n",
    "            nn.Sigmoid(),\n",
    "        ])\n",
    "        self.network = nn.Sequential(*self.network)\n",
    "    def forward(self, z):\n",
    "        x_recon = self.network(z)\n",
    "        return x_recon\n",
    "\n",
    "class Discriminator(nn.Module):\n",
    "    def __init__(self, dims, dim_h):\n",
    "        super(Discriminator,self).__init__()\n",
    "        self.dim_z = dims\n",
    "        self.dim_h = dim_h\n",
    "        self.network = []\n",
    "        self.network.extend([\n",
    "            nn.Linear(self.dim_z, self.dim_h),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(p=0.2),                         \n",
    "            nn.Linear(self.dim_h, self.dim_h),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(p=0.2),                         \n",
    "            nn.Linear(self.dim_h, 1),\n",
    "            nn.Sigmoid(),\n",
    "        ])\n",
    "        self.network = nn.Sequential(*self.network)\n",
    "\n",
    "    def forward(self, z):\n",
    "        disc = self.network(z)\n",
    "        return disc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "26owCMpC2yuJ"
   },
   "outputs": [],
   "source": [
    "def one_hot_encoding(labels, n_classes=10):\n",
    "    cat = np.array(labels.data.tolist())\n",
    "    cat = np.eye(n_classes)[cat].astype('float32')\n",
    "    cat = torch.from_numpy(cat)\n",
    "    return autograd.Variable(cat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DkvFSgO4846R"
   },
   "outputs": [],
   "source": [
    "autoencoder_loss = nn.MSELoss()\n",
    "\n",
    "def train_validate(\n",
    "        encoder,\n",
    "        decoder,\n",
    "        discriminator,\n",
    "        dataloader,\n",
    "        optim_encoder,\n",
    "        optim_decoder,\n",
    "        optim_D,\n",
    "        train\n",
    "    ):\n",
    "    total_rec_loss = 0\n",
    "    total_disc_loss = 0\n",
    "    total_gen_loss = 0\n",
    "    if train:\n",
    "        encoder.train()\n",
    "        decoder.train()\n",
    "        discriminator.train()\n",
    "    else:\n",
    "        encoder.eval()\n",
    "        decoder.eval()\n",
    "        discriminator.eval()\n",
    "\n",
    "    iteration = 0\n",
    "    for (data, labels) in dataloader:\n",
    "        # Reconstruction loss:\n",
    "        for p in discriminator.parameters():\n",
    "            p.requires_grad = False\n",
    "\n",
    "        real_data = autograd.Variable(data).to(device).view(-1, 784)\n",
    "        encoding = encoder(real_data)\n",
    "\n",
    "        if decoder.supervised:\n",
    "            label_hot = one_hot_encoding(labels, n_classes=10).to(device)\n",
    "            restored_image = decoder(torch.cat((label_hot, encoding), 1))\n",
    "        else:\n",
    "            restored_image = decoder(encoding)\n",
    "\n",
    "        # autoencoder_loss or F.binary_cross_entropy\n",
    "        reconstruction_loss = autoencoder_loss(restored_image, real_data)\n",
    "        total_rec_loss += reconstruction_loss.item()\n",
    "        if train:\n",
    "            optim_encoder.zero_grad()\n",
    "            optim_decoder.zero_grad()\n",
    "            reconstruction_loss.backward()\n",
    "            optim_encoder.step()\n",
    "            optim_decoder.step()\n",
    "\n",
    "        # Discriminator loss:\n",
    "        # i) latent representation\n",
    "        encoder.eval()\n",
    "        z_real_gauss = autograd.Variable(\n",
    "            torch.randn(data.size()[0], dims)\n",
    "        ).to(device)\n",
    "        z_fake_gauss = encoder(real_data)\n",
    "        # ii) feed into discriminator\n",
    "        D_real_gauss = discriminator(z_real_gauss)\n",
    "        D_fake_gauss = discriminator(z_fake_gauss)\n",
    "\n",
    "        D_loss = -torch.mean(  # cross entropy\n",
    "            torch.log(D_real_gauss + EPS) +\n",
    "            torch.log(1 - D_fake_gauss + EPS)\n",
    "        )\n",
    "        total_disc_loss += D_loss.item()\n",
    "\n",
    "        if train:\n",
    "            optim_D.zero_grad()\n",
    "            D_loss.backward()\n",
    "            optim_D.step()\n",
    "\n",
    "        # Generator (encoder) loss\n",
    "        if train:\n",
    "            encoder.train()\n",
    "        else:\n",
    "            encoder.eval()\n",
    "        z_fake_gauss = encoder(real_data)\n",
    "        D_fake_gauss = discriminator(z_fake_gauss)\n",
    "\n",
    "        G_loss = -torch.mean(torch.log(D_fake_gauss + EPS))\n",
    "        total_gen_loss += G_loss.item()\n",
    "\n",
    "        if train:\n",
    "            optim_encoder_reg.zero_grad()\n",
    "            G_loss.backward()\n",
    "            optim_encoder_reg.step()\n",
    "\n",
    "        if (iteration % 100) == 0:\n",
    "            print(\n",
    "                'reconstruction loss: %.4f, discriminator loss: %.4f, generator loss: %.4f' %\n",
    "                (reconstruction_loss.item(), D_loss.item(), G_loss.item()))\n",
    "            \n",
    "        iteration += 1\n",
    "\n",
    "    M = len(dataloader.dataset)\n",
    "    return total_rec_loss / M, total_disc_loss / M, total_gen_loss / M\n",
    "              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ew1_l2JR9Sol"
   },
   "outputs": [],
   "source": [
    "encoder = Encoder(784, 1000, dims).to(device)      \n",
    "decoder = Decoder(784, 1000, dims, supervised=supervised).to(device)\n",
    "discriminator = Discriminator(dims, 500).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aDlhDta89s0W"
   },
   "outputs": [],
   "source": [
    "lr = 0.00025"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1F_bcZ1S9V2D"
   },
   "outputs": [],
   "source": [
    "#encode/decode optimizers\n",
    "optim_encoder = torch.optim.Adam(encoder.parameters(), lr=lr)\n",
    "optim_decoder = torch.optim.Adam(decoder.parameters(), lr=lr)\n",
    "optim_D = torch.optim.Adam(discriminator.parameters(), lr=lr)\n",
    "optim_encoder_reg = torch.optim.Adam(encoder.parameters(), lr=lr * 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "04c-NtiJ96x_"
   },
   "outputs": [],
   "source": [
    "n_epochs = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9fKQW3GtA27V"
   },
   "outputs": [],
   "source": [
    "#encoder.load_state_dict(torch.load(Model_dir + 'encoder_z' + str(dims)+'_epch' + str(100) + '.pt'))\n",
    "#decoder.load_state_dict(torch.load(Model_dir +'decoder_z' + str(dims)+'_epch' + str(100) + '.pt'))\n",
    "#discriminator.load_state_dict(torch.load(Model_dir +'disc_z' + str(dims)+'_epch'+str(100) + '.pt'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "c2c4d7e04a0c4454810005ea48825e67",
      "d0e4269289e24b06bc41ad817a591c40",
      "ca66f7f7fcbd4eb48f9b177071d4d266",
      "19c6b20130d74c69a637fbdf4299d504",
      "05ba0fd37d2e41be80484def973b084e",
      "d5746226bd6948c6b8ef1c005a1fcf5b",
      "ac3d1fed8e8b4128bcd2a6fc4c1fc733",
      "d7be0fa5d4814d9f96df68b59eea66ce"
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    },
    "colab_type": "code",
    "id": "IIC0dMvR9qbZ",
    "outputId": "e7ac1b09-60f7-4a94-a0a9-09400ba806a9"
   },
   "outputs": [
    {
     "data": {
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       "model_id": "c2c4d7e04a0c4454810005ea48825e67",
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       "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reconstruction loss: 1.2567, discriminator loss: 1.4004, generator loss: 0.7610\n",
      "epoch: 0 ---- training loss: 0.00084947\n",
      "reconstruction loss: 0.7448, discriminator loss: 1.0729, generator loss: 1.3378\n",
      "epoch: 0 ---- validation loss: 0.00077451\n",
      "reconstruction loss: 0.7877, discriminator loss: 1.0814, generator loss: 1.4391\n",
      "epoch: 1 ---- training loss: 0.00074236\n",
      "reconstruction loss: 0.7339, discriminator loss: 1.1838, generator loss: 1.1680\n",
      "epoch: 2 ---- training loss: 0.00070829\n",
      "reconstruction loss: 0.6965, discriminator loss: 1.2426, generator loss: 1.0365\n",
      "epoch: 3 ---- training loss: 0.00067801\n",
      "reconstruction loss: 0.6790, discriminator loss: 1.2719, generator loss: 0.9977\n",
      "epoch: 4 ---- training loss: 0.00065683\n",
      "reconstruction loss: 0.6615, discriminator loss: 1.2960, generator loss: 0.9432\n",
      "epoch: 5 ---- training loss: 0.00064449\n",
      "reconstruction loss: 0.6218, discriminator loss: 1.3282, generator loss: 0.8504\n",
      "epoch: 5 ---- validation loss: 0.00064946\n",
      "reconstruction loss: 0.6587, discriminator loss: 1.3171, generator loss: 0.9154\n",
      "epoch: 6 ---- training loss: 0.00063685\n",
      "reconstruction loss: 0.6451, discriminator loss: 1.3253, generator loss: 0.8996\n",
      "epoch: 7 ---- training loss: 0.00063102\n",
      "reconstruction loss: 0.6423, discriminator loss: 1.3345, generator loss: 0.8902\n",
      "epoch: 8 ---- training loss: 0.00062651\n",
      "reconstruction loss: 0.6401, discriminator loss: 1.3326, generator loss: 0.8911\n",
      "epoch: 9 ---- training loss: 0.00062280\n",
      "reconstruction loss: 0.6278, discriminator loss: 1.3388, generator loss: 0.8736\n",
      "epoch: 10 ---- training loss: 0.00062027\n",
      "reconstruction loss: 0.6011, discriminator loss: 1.3568, generator loss: 0.8095\n",
      "epoch: 10 ---- validation loss: 0.00062910\n",
      "reconstruction loss: 0.6326, discriminator loss: 1.3471, generator loss: 0.8724\n",
      "epoch: 11 ---- training loss: 0.00061794\n",
      "reconstruction loss: 0.6191, discriminator loss: 1.3454, generator loss: 0.8727\n",
      "epoch: 12 ---- training loss: 0.00061585\n",
      "reconstruction loss: 0.6265, discriminator loss: 1.3376, generator loss: 0.8703\n",
      "epoch: 13 ---- training loss: 0.00061410\n",
      "reconstruction loss: 0.6264, discriminator loss: 1.3481, generator loss: 0.8608\n",
      "epoch: 14 ---- training loss: 0.00061199\n",
      "reconstruction loss: 0.6189, discriminator loss: 1.3412, generator loss: 0.8738\n",
      "epoch: 15 ---- training loss: 0.00061062\n",
      "reconstruction loss: 0.5935, discriminator loss: 1.3635, generator loss: 0.7989\n",
      "epoch: 15 ---- validation loss: 0.00062103\n",
      "reconstruction loss: 0.6181, discriminator loss: 1.3560, generator loss: 0.8742\n",
      "epoch: 16 ---- training loss: 0.00060957\n",
      "reconstruction loss: 0.6200, discriminator loss: 1.3553, generator loss: 0.8532\n",
      "epoch: 17 ---- training loss: 0.00060867\n",
      "reconstruction loss: 0.6177, discriminator loss: 1.3547, generator loss: 0.8587\n",
      "epoch: 18 ---- training loss: 0.00060780\n",
      "reconstruction loss: 0.6255, discriminator loss: 1.3445, generator loss: 0.8700\n",
      "epoch: 19 ---- training loss: 0.00060701\n",
      "reconstruction loss: 0.6149, discriminator loss: 1.3562, generator loss: 0.8469\n",
      "epoch: 20 ---- training loss: 0.00060637\n",
      "reconstruction loss: 0.5900, discriminator loss: 1.3716, generator loss: 0.7892\n",
      "epoch: 20 ---- validation loss: 0.00061740\n",
      "reconstruction loss: 0.6256, discriminator loss: 1.3467, generator loss: 0.8593\n",
      "epoch: 21 ---- training loss: 0.00060546\n",
      "reconstruction loss: 0.6106, discriminator loss: 1.3510, generator loss: 0.8647\n",
      "epoch: 22 ---- training loss: 0.00060457\n",
      "reconstruction loss: 0.6244, discriminator loss: 1.3538, generator loss: 0.8464\n",
      "epoch: 23 ---- training loss: 0.00060359\n",
      "reconstruction loss: 0.6142, discriminator loss: 1.3614, generator loss: 0.8606\n",
      "epoch: 24 ---- training loss: 0.00060307\n",
      "reconstruction loss: 0.6094, discriminator loss: 1.3563, generator loss: 0.8521\n",
      "epoch: 25 ---- training loss: 0.00060217\n",
      "reconstruction loss: 0.5872, discriminator loss: 1.3739, generator loss: 0.7864\n",
      "epoch: 25 ---- validation loss: 0.00061398\n",
      "reconstruction loss: 0.6204, discriminator loss: 1.3553, generator loss: 0.8469\n",
      "epoch: 26 ---- training loss: 0.00060154\n",
      "reconstruction loss: 0.6162, discriminator loss: 1.3525, generator loss: 0.8710\n",
      "epoch: 27 ---- training loss: 0.00060108\n",
      "reconstruction loss: 0.6083, discriminator loss: 1.3492, generator loss: 0.8404\n",
      "epoch: 28 ---- training loss: 0.00060057\n",
      "reconstruction loss: 0.6134, discriminator loss: 1.3654, generator loss: 0.8405\n",
      "epoch: 29 ---- training loss: 0.00060015\n",
      "reconstruction loss: 0.6147, discriminator loss: 1.3608, generator loss: 0.8504\n",
      "epoch: 30 ---- training loss: 0.00059963\n",
      "reconstruction loss: 0.5854, discriminator loss: 1.3773, generator loss: 0.7815\n",
      "epoch: 30 ---- validation loss: 0.00061211\n",
      "reconstruction loss: 0.6003, discriminator loss: 1.3561, generator loss: 0.8366\n",
      "epoch: 31 ---- training loss: 0.00059909\n",
      "reconstruction loss: 0.6154, discriminator loss: 1.3554, generator loss: 0.8512\n",
      "epoch: 32 ---- training loss: 0.00059868\n",
      "reconstruction loss: 0.6062, discriminator loss: 1.3581, generator loss: 0.8557\n",
      "epoch: 33 ---- training loss: 0.00059819\n",
      "reconstruction loss: 0.6045, discriminator loss: 1.3529, generator loss: 0.8490\n",
      "epoch: 34 ---- training loss: 0.00059776\n",
      "reconstruction loss: 0.6109, discriminator loss: 1.3526, generator loss: 0.8513\n",
      "epoch: 35 ---- training loss: 0.00059730\n",
      "reconstruction loss: 0.5836, discriminator loss: 1.3786, generator loss: 0.7809\n",
      "epoch: 35 ---- validation loss: 0.00060996\n",
      "reconstruction loss: 0.6067, discriminator loss: 1.3558, generator loss: 0.8389\n",
      "epoch: 36 ---- training loss: 0.00059658\n",
      "reconstruction loss: 0.6024, discriminator loss: 1.3491, generator loss: 0.8473\n",
      "epoch: 37 ---- training loss: 0.00059632\n",
      "reconstruction loss: 0.6036, discriminator loss: 1.3569, generator loss: 0.8405\n",
      "epoch: 38 ---- training loss: 0.00059588\n",
      "reconstruction loss: 0.6092, discriminator loss: 1.3588, generator loss: 0.8407\n",
      "epoch: 39 ---- training loss: 0.00059570\n",
      "reconstruction loss: 0.6112, discriminator loss: 1.3608, generator loss: 0.8345\n",
      "epoch: 40 ---- training loss: 0.00059542\n",
      "reconstruction loss: 0.5823, discriminator loss: 1.3825, generator loss: 0.7787\n",
      "epoch: 40 ---- validation loss: 0.00060860\n",
      "reconstruction loss: 0.5992, discriminator loss: 1.3608, generator loss: 0.8385\n",
      "epoch: 41 ---- training loss: 0.00059495\n",
      "reconstruction loss: 0.6090, discriminator loss: 1.3641, generator loss: 0.8348\n",
      "epoch: 42 ---- training loss: 0.00059468\n",
      "reconstruction loss: 0.6019, discriminator loss: 1.3650, generator loss: 0.8405\n",
      "epoch: 43 ---- training loss: 0.00059416\n",
      "reconstruction loss: 0.6189, discriminator loss: 1.3551, generator loss: 0.8440\n",
      "epoch: 44 ---- training loss: 0.00059400\n",
      "reconstruction loss: 0.5993, discriminator loss: 1.3614, generator loss: 0.8383\n",
      "epoch: 45 ---- training loss: 0.00059355\n",
      "reconstruction loss: 0.5816, discriminator loss: 1.3810, generator loss: 0.7765\n",
      "epoch: 45 ---- validation loss: 0.00060770\n",
      "reconstruction loss: 0.6043, discriminator loss: 1.3621, generator loss: 0.8409\n",
      "epoch: 46 ---- training loss: 0.00059339\n",
      "reconstruction loss: 0.6103, discriminator loss: 1.3626, generator loss: 0.8392\n",
      "epoch: 47 ---- training loss: 0.00059310\n",
      "reconstruction loss: 0.6107, discriminator loss: 1.3585, generator loss: 0.8410\n",
      "epoch: 48 ---- training loss: 0.00059275\n",
      "reconstruction loss: 0.6052, discriminator loss: 1.3646, generator loss: 0.8413\n",
      "epoch: 49 ---- training loss: 0.00059236\n",
      "reconstruction loss: 0.6038, discriminator loss: 1.3556, generator loss: 0.8437\n",
      "epoch: 50 ---- training loss: 0.00059225\n",
      "reconstruction loss: 0.5803, discriminator loss: 1.3807, generator loss: 0.7751\n",
      "epoch: 50 ---- validation loss: 0.00060628\n",
      "reconstruction loss: 0.5972, discriminator loss: 1.3517, generator loss: 0.8395\n",
      "epoch: 51 ---- training loss: 0.00059203\n",
      "reconstruction loss: 0.6030, discriminator loss: 1.3620, generator loss: 0.8321\n",
      "epoch: 52 ---- training loss: 0.00059180\n",
      "reconstruction loss: 0.6047, discriminator loss: 1.3649, generator loss: 0.8569\n",
      "epoch: 53 ---- training loss: 0.00059155\n",
      "reconstruction loss: 0.6038, discriminator loss: 1.3659, generator loss: 0.8416\n",
      "epoch: 54 ---- training loss: 0.00059118\n",
      "reconstruction loss: 0.5890, discriminator loss: 1.3511, generator loss: 0.8392\n",
      "epoch: 55 ---- training loss: 0.00059106\n",
      "reconstruction loss: 0.5797, discriminator loss: 1.3844, generator loss: 0.7749\n",
      "epoch: 55 ---- validation loss: 0.00060568\n",
      "reconstruction loss: 0.5983, discriminator loss: 1.3572, generator loss: 0.8309\n",
      "epoch: 56 ---- training loss: 0.00059072\n",
      "reconstruction loss: 0.5979, discriminator loss: 1.3654, generator loss: 0.8262\n",
      "epoch: 57 ---- training loss: 0.00059049\n",
      "reconstruction loss: 0.6000, discriminator loss: 1.3614, generator loss: 0.8265\n",
      "epoch: 58 ---- training loss: 0.00059034\n",
      "reconstruction loss: 0.6086, discriminator loss: 1.3641, generator loss: 0.8473\n",
      "epoch: 59 ---- training loss: 0.00059013\n",
      "reconstruction loss: 0.6031, discriminator loss: 1.3640, generator loss: 0.8395\n",
      "epoch: 60 ---- training loss: 0.00058984\n",
      "reconstruction loss: 0.5789, discriminator loss: 1.3850, generator loss: 0.7727\n",
      "epoch: 60 ---- validation loss: 0.00060471\n",
      "reconstruction loss: 0.6100, discriminator loss: 1.3621, generator loss: 0.8273\n",
      "epoch: 61 ---- training loss: 0.00058974\n",
      "reconstruction loss: 0.5855, discriminator loss: 1.3700, generator loss: 0.8223\n",
      "epoch: 62 ---- training loss: 0.00058948\n",
      "reconstruction loss: 0.6031, discriminator loss: 1.3630, generator loss: 0.8448\n",
      "epoch: 63 ---- training loss: 0.00058925\n",
      "reconstruction loss: 0.6015, discriminator loss: 1.3530, generator loss: 0.8363\n",
      "epoch: 64 ---- training loss: 0.00058919\n",
      "reconstruction loss: 0.5949, discriminator loss: 1.3587, generator loss: 0.8414\n",
      "epoch: 65 ---- training loss: 0.00058907\n",
      "reconstruction loss: 0.5783, discriminator loss: 1.3860, generator loss: 0.7716\n",
      "epoch: 65 ---- validation loss: 0.00060401\n",
      "reconstruction loss: 0.5936, discriminator loss: 1.3685, generator loss: 0.8347\n",
      "epoch: 66 ---- training loss: 0.00058893\n",
      "reconstruction loss: 0.5969, discriminator loss: 1.3673, generator loss: 0.8218\n",
      "epoch: 67 ---- training loss: 0.00058872\n",
      "reconstruction loss: 0.6003, discriminator loss: 1.3609, generator loss: 0.8356\n",
      "epoch: 68 ---- training loss: 0.00058845\n",
      "reconstruction loss: 0.5867, discriminator loss: 1.3589, generator loss: 0.8193\n",
      "epoch: 69 ---- training loss: 0.00058836\n",
      "reconstruction loss: 0.5937, discriminator loss: 1.3673, generator loss: 0.8249\n",
      "epoch: 70 ---- training loss: 0.00058807\n",
      "reconstruction loss: 0.5782, discriminator loss: 1.3875, generator loss: 0.7693\n",
      "epoch: 70 ---- validation loss: 0.00060348\n",
      "reconstruction loss: 0.5870, discriminator loss: 1.3662, generator loss: 0.8187\n",
      "epoch: 71 ---- training loss: 0.00058797\n",
      "reconstruction loss: 0.5874, discriminator loss: 1.3638, generator loss: 0.8364\n",
      "epoch: 72 ---- training loss: 0.00058781\n",
      "reconstruction loss: 0.5909, discriminator loss: 1.3698, generator loss: 0.8318\n",
      "epoch: 73 ---- training loss: 0.00058763\n",
      "reconstruction loss: 0.5969, discriminator loss: 1.3689, generator loss: 0.8338\n",
      "epoch: 74 ---- training loss: 0.00058754\n",
      "reconstruction loss: 0.5886, discriminator loss: 1.3674, generator loss: 0.8103\n",
      "epoch: 75 ---- training loss: 0.00058745\n",
      "reconstruction loss: 0.5772, discriminator loss: 1.3899, generator loss: 0.7689\n",
      "epoch: 75 ---- validation loss: 0.00060287\n",
      "reconstruction loss: 0.6001, discriminator loss: 1.3628, generator loss: 0.8282\n",
      "epoch: 76 ---- training loss: 0.00058715\n",
      "reconstruction loss: 0.5935, discriminator loss: 1.3626, generator loss: 0.8300\n",
      "epoch: 77 ---- training loss: 0.00058700\n",
      "reconstruction loss: 0.5979, discriminator loss: 1.3653, generator loss: 0.8241\n",
      "epoch: 78 ---- training loss: 0.00058678\n",
      "reconstruction loss: 0.6032, discriminator loss: 1.3701, generator loss: 0.8267\n",
      "epoch: 79 ---- training loss: 0.00058670\n",
      "reconstruction loss: 0.5905, discriminator loss: 1.3638, generator loss: 0.8276\n",
      "epoch: 80 ---- training loss: 0.00058661\n",
      "reconstruction loss: 0.5771, discriminator loss: 1.3879, generator loss: 0.7678\n",
      "epoch: 80 ---- validation loss: 0.00060250\n",
      "reconstruction loss: 0.5981, discriminator loss: 1.3695, generator loss: 0.8349\n",
      "epoch: 81 ---- training loss: 0.00058644\n",
      "reconstruction loss: 0.5914, discriminator loss: 1.3619, generator loss: 0.8313\n",
      "epoch: 82 ---- training loss: 0.00058631\n",
      "reconstruction loss: 0.5996, discriminator loss: 1.3730, generator loss: 0.8237\n",
      "epoch: 83 ---- training loss: 0.00058612\n",
      "reconstruction loss: 0.5961, discriminator loss: 1.3671, generator loss: 0.8382\n",
      "epoch: 84 ---- training loss: 0.00058604\n",
      "reconstruction loss: 0.6018, discriminator loss: 1.3633, generator loss: 0.8226\n",
      "epoch: 85 ---- training loss: 0.00058597\n",
      "reconstruction loss: 0.5771, discriminator loss: 1.3897, generator loss: 0.7663\n",
      "epoch: 85 ---- validation loss: 0.00060205\n",
      "reconstruction loss: 0.5912, discriminator loss: 1.3655, generator loss: 0.8142\n",
      "epoch: 86 ---- training loss: 0.00058594\n",
      "reconstruction loss: 0.5920, discriminator loss: 1.3777, generator loss: 0.8250\n",
      "epoch: 87 ---- training loss: 0.00058568\n",
      "reconstruction loss: 0.5921, discriminator loss: 1.3704, generator loss: 0.8218\n",
      "epoch: 88 ---- training loss: 0.00058559\n",
      "reconstruction loss: 0.5879, discriminator loss: 1.3813, generator loss: 0.8351\n",
      "epoch: 89 ---- training loss: 0.00058554\n",
      "reconstruction loss: 0.5814, discriminator loss: 1.3756, generator loss: 0.8282\n",
      "epoch: 90 ---- training loss: 0.00058547\n",
      "reconstruction loss: 0.5769, discriminator loss: 1.3928, generator loss: 0.7645\n",
      "epoch: 90 ---- validation loss: 0.00060214\n",
      "reconstruction loss: 0.5921, discriminator loss: 1.3694, generator loss: 0.8370\n",
      "epoch: 91 ---- training loss: 0.00058534\n",
      "reconstruction loss: 0.5966, discriminator loss: 1.3650, generator loss: 0.8227\n",
      "epoch: 92 ---- training loss: 0.00058524\n",
      "reconstruction loss: 0.5984, discriminator loss: 1.3720, generator loss: 0.8199\n",
      "epoch: 93 ---- training loss: 0.00058517\n",
      "reconstruction loss: 0.5915, discriminator loss: 1.3701, generator loss: 0.8224\n",
      "epoch: 94 ---- training loss: 0.00058510\n",
      "reconstruction loss: 0.5896, discriminator loss: 1.3796, generator loss: 0.8253\n",
      "epoch: 95 ---- training loss: 0.00058499\n",
      "reconstruction loss: 0.5764, discriminator loss: 1.3908, generator loss: 0.7654\n",
      "epoch: 95 ---- validation loss: 0.00060162\n",
      "reconstruction loss: 0.5922, discriminator loss: 1.3685, generator loss: 0.8260\n",
      "epoch: 96 ---- training loss: 0.00058487\n",
      "reconstruction loss: 0.5861, discriminator loss: 1.3693, generator loss: 0.8348\n",
      "epoch: 97 ---- training loss: 0.00058477\n",
      "reconstruction loss: 0.5972, discriminator loss: 1.3620, generator loss: 0.8287\n",
      "epoch: 98 ---- training loss: 0.00058474\n",
      "reconstruction loss: 0.5896, discriminator loss: 1.3791, generator loss: 0.8204\n",
      "epoch: 99 ---- training loss: 0.00058453\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train_loss = []\n",
    "val_loss = []\n",
    "for epoch in trange(n_epochs):\n",
    "    l1, l2, l3 = train_validate(\n",
    "        encoder, decoder, discriminator,\n",
    "        train_loader, optim_encoder, optim_decoder,\n",
    "        optim_D, train=True\n",
    "    )\n",
    "    print('epoch: {} ---- training loss: {:.8f}'.format(epoch, l1))\n",
    "    train_loss.append(l1)\n",
    "\n",
    "    if (epoch % 5) == 0:\n",
    "        l1, l2, l3 = train_validate(\n",
    "            encoder, decoder, discriminator,\n",
    "            val_loader, optim_encoder,\n",
    "            optim_decoder, optim_D, False\n",
    "        )\n",
    "        print('epoch: {} ---- validation loss: {:.8f}'.format(epoch, l1))\n",
    "        val_loss.append(l1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yT98bCMeeoGs"
   },
   "source": [
    "it's interesting to see how generator loss and discriminator loss drive each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 355
    },
    "colab_type": "code",
    "id": "HCTCEj6H95Zk",
    "outputId": "2fd1ae1b-7529-4e7e-b47b-f372129dde87"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()\n",
    "plt.rcParams['figure.figsize'] = 5, 5\n",
    "plt.plot(np.arange(len(train_loss)), train_loss, label='train')\n",
    "plt.plot(np.arange(0, len(val_loss) * 5, 5), val_loss, label='val')\n",
    "plt.title('Training')\n",
    "plt.xlabel('Step')\n",
    "plt.ylabel('Reconstruction loss')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig(Fig_dir + 'training_' + str(n_epochs) + 'epochs.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "j3wjevto_eXH"
   },
   "outputs": [],
   "source": [
    "#torch.save(encoder.state_dict(), Model_dir + 'encoder_z' + str(dims)+'_epch' + str(n_epochs) + '.pt')\n",
    "#torch.save(decoder.state_dict(), Model_dir +'decoder_z' + str(dims)+'_epch' + str(n_epochs) + '.pt')\n",
    "#torch.save(discriminator.state_dict(), Model_dir +'disc_z' + str(dims)+'_epch'+str(n_epochs) + '.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rVbiIP9axI1S"
   },
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "\n",
    "def plot_vis(X_enc, y, dims=2):\n",
    "    '''Performing t-SNE dimensionality reduction\n",
    "    and plot in 2 or 3 dimensions.\n",
    "    '''\n",
    "    X_tsne = TSNE(n_components=dims).fit_transform(X_enc)\n",
    "    cmap = plt.get_cmap('rainbow', 10)\n",
    "\n",
    "    if dims == 2:\n",
    "      plt.scatter(\n",
    "          X_tsne[:, 0], X_tsne[:, 1], \n",
    "          c=y, edgecolor='black', cmap=cmap\n",
    "      )\n",
    "    else:\n",
    "        fig = plt.figure()\n",
    "        ax = plt.axes(projection='3d')\n",
    "        p = ax.scatter3D(\n",
    "            X_tsne[:, 0], X_tsne[:, 1], X_tsne[:, 2], \n",
    "            c=y, cmap=cmap, edgecolor='black'\n",
    "        )\n",
    "        fig.colorbar(p, drawedges=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VfyggSMS1Q-T"
   },
   "outputs": [],
   "source": [
    "# visualization of the unsupervised case\n",
    "# Does our encoder distinguish the classes?\n",
    "import math\n",
    "n_points = 1000\n",
    "\n",
    "X = []\n",
    "y = []\n",
    "for (data, labels) in val_loader:\n",
    "  images = autograd.Variable(data).to(device).view(-1, 784)\n",
    "  X.append(encoder(images).cpu().data.numpy())\n",
    "  y.append(labels.cpu().data.numpy())\n",
    "  if len(X) * images.shape[0] > n_points:\n",
    "      break\n",
    "\n",
    "X = np.concatenate(X, 0)\n",
    "y = np.concatenate(y, 0)\n",
    "X = X[:n_points, :]\n",
    "y = y[:n_points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 322
    },
    "colab_type": "code",
    "id": "8XlG88eFbazP",
    "outputId": "2e509f8f-b207-4a19-e066-a0bb136f8322"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_vis(X, y, dims=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "colab_type": "code",
    "id": "jBtMo496bZci",
    "outputId": "181e7823-c8e8-4934-cac3-5049459d21fb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9jpzO8wmnnp2VW9bR+prBHM4/QkyPVgiAK/Uo+7KKERwurB4vazdl0fXq63jqqecQRZGvP/mQrgE7bY0ioQ4bXxwuZEZmCUM6xLB5bxEDJ16DOa4F9vxcDIlxfDl3AdsWfUVAJyJozUz944OMnzC50b7Tb6m2LhvfuvD5/LhcHkRRxGZz/C407vF4GiGn7PJ1/NaHZisy9d8D0zBb+FUqFVqtGp/P3+ilDPyBILIoU1lWSGK8lbRdGRgkkSKTleWr99G5TUeuu/sWxo+fiCAIuFwutq9YzO3xRnKyi9mSXUYgPBKxX0fWZuVTqo0ma28+rWOScEsRZPyajg4v0T3i6D28I/mFlcz++E1kRNIzDuOwO8gvrSDtYCoqWzmde3bn4aeeISsrk/T0NKKiYhg2bBiSJFFVVYXFYkGSpGP1gx2YTNU3MVT7lDIzM/H5vLRu3eYUX1BT34e/D41rMBoNhISojrWTaZjWv5faZrzGolmKzLmUaWgIS6YmD6nhnnhn9hP1bNeZBT+tQRuhovxQLt6dh4i06CjYncXTj/4VvV5PZWUVOTk5tGjRgu3btuCsKKJ9ny5E6RLIPpiHMSmGzp1aEbToqdKr2b+xiMObvkFjDqeqUo9V42XMzf1JSbDi9PrIyS3nqaf/j/6ju5CWZqM8ahBxHa7Bl/4r6atXMuHnX3BoLFhCVIRYNEx/60XM0e3RhsdgFINcP3IIa36YCy47gsHM7Y88RVRUFHc++BC7M3IwVhVgDLrpP3w0t951L7169Wygc3l2agspV4fGq+sbV4fGNej11a1/vV7/8do45yI4l/M+mfrQ7ETmfOrAnI8lo9FI6PXV7UokSXX8CX0+nO0H2L9vP/x+H7M/fZcEi0QHazgyYG7ZmVXbN1Oqd2Mwavls/qd0ik0mL+MAxlA9M7am0S1cS7mkIz81lyGTRyAKQTTrNqAxtsdhjsWWn4pcmY+gDrIrz86GAg+iCBUuP1azjs6JoRxytqBdt6GEeP3o2rTh0Pv70PuKMXeM5/b/m4TBpCdnx68sWZJB91tmUJmdxowX/8TMcQPoHJvCvsJS3n39ZZwh0ewWomhhLubBbp0x2EpZt2c1D92zAltQzb133scDD9aeZNeUVIfGz9z6t7bQ+OloqqXgxU6zEpnzi7aeW6Ep+H27EpVK1SROPUEQGDxoMC2TW/LTd3NwVJRiCAmjXUoCq8v306VVApWHj5DnLEWVX8yU7vGogl4W7ylmQ9BMuseJsbiShf+cTUy4hfZ+sGWsIqRbbyz9Uqjol0zm/95i+fIjdJoyjqqCIrZvzCXZHEWk0Bo5kE9AVd2KVSUJCCoBrygiyV6+mPk1sgxduiYSZtVydP0SMreswFNcxtSPvydcK3F9yyiyCuzsCRoI6XMtvZwibSLCCRjU3BWlJjO1nKheHXnzo4+Ji49n4sTG9QXVZ3Pc6Vr/RkQYCAQCx//vTKFxZblUN5qFyNREkM5nyXOuF/tCtysBiI+P508PPYXf70eWg3z93bd4KirgQAmjO8bh3yvRK0ZHh+RYysrLiTaXsf3gUXpaDPxx8ggyduxmWXouoX0G0ipOh3vYCNLLncjGcII6C66oK9izMhtUejxtryZr5Y+4HAHCbZlk7lxBmSkG1ZGt+G0llKgEQoJuhr4wDUEW2PT02/jyyjFmzwY/XJcczj2tIyktK+alfXkUI9K3ZQgHHIV45CA+fwCVx07QIIFKomuvNqzeUcEPC+Y3gcic+3trWv9WVf0+NF5TiKu2SJXi+D07F1xkGq4OTP0dvwaDFpWqtjykC1MHRpIkfD4vXdp34qtvPqDjTb3Q4MPtcBGmNyPLMl3ateabzTmIKjO3DB+MPxCg54hhxHeuInrMjbz5+SfkFJSj6j4SQVAhqQQ0YTGYO12DHPQjzJqONdCLhXOyydccweVPpVLUEXTaCKrVRCWH0+HBScghZmRRJOHmMaS/8jER8fEU7dnB7QMSQRCJMhvpF67n23w7fcJkjhxez69OP6qSHNqZVRzU6Ok8qi8L5qyAghL2HHVx18MPklmQj16SmHLV1dx0081otdoGPIMNY1mcHBqXpOqs8ZAQE4Jwauvfc7Vkrr/+WjQaDRpN9Xe///6H6NdvwPkfeDPlgotMQ+2Bqa8VZDTqEASh1jSBhgqHV89T/4nat+9Am5iW7Fmxg1ZRZmJFgQUbs+hYBlpTOUF9OK3bh2IIDScqxExpWSkrt27FVVCGWVZTuXIVgh38NieS3kj5d2/iyR6FO2074cFSvO0kCg+Xcf34SXy2YD4RTz9JyOBBVK1aQ+nrr+EutYHBQhAVbrsXW4WDw+uWgAA3LS2iVaiJqqAPm9tHldbKV1I/onuoqNy4nMVFpSzODxISEYr8+Qr6xhu595pEcottvLd5NdZ/vYwj/ygvvvsGb3zwPx6++wHuuP2O8z/ZNE6lOr/fj93uP6X1r9ls4L777sPj8TBo0BAGDx5GdHRMveZ94YV/07Jl6wY91ubKBReZC5UmIMsydns9etOcE+f+g+/ZvS/Zi7OIDjMRHmtgbvp+fqgooF2PSIZPupGYuAQ+mzObUHclq7ZvI7p/W8ZdlcznH69DWxDAk20jEJGMK2kAwpFf8Kz6hk5juxN/zXjce/fgy6hg0Y/fIyXEEzNhLIgC2snjKH5nJvvenofPI+NzuEh9dy4xJom/PjOauAQrMz/axKZ16bz46kT27i/g83lp2KxtKJXUaDtV0MN/GAwSaetSEZGZNqw9XSMtyG3CSC9y8tPHH+KXNOhGX42o1fLSJx+yYtlS3pv1IQaDoQHPfcNzcuvfu+66h5Urf+arr+bwxRefM2/ej0ou02m44CLTUFT3Jj5zRKgmTSAQCOB01j9NoCmJa5FIRFRLgnY1kiBwW78rSW3bjmtv/QM+n5eZ//kP3y5ehNPtYsiwttx05yDi4uKJjArh+ecWQvE2MrbMw12cj3ZwHyJ7dSB+cCvIyiA5Jpqjdi9eMUigpJSgy4lkMeMtKwWfD2NyG0oOuig5kI3B6WbsdZ0Z1CcREHjgvkFsXp/B0SI7o8Z1ZfnKbArzslCV7WVwfIDBnWMwtghlZkYh2TnlROk06CQRm9eH2+3DfGQPngmTCenfF41eQ2SnVux79x0m3HAN78+YTUpKy3M+Z02ZFd2qVWsGDuzHvfc+jNPpqrfAPP/8XwGZLl26c++9D2I2mxvnQJsBF6Dt2ak0ZSOu6nYl/rMKTMPmQdWPms/t1rsP2XHx+AUVCCq2abUMGDMWSZL4dMZbfL/4K+6+vz+jR7cnNFRHaUEeJcXFBIMyEeFW5n40i+0rl/Pth7PRHDhE5nufkfPpXEJdLgorSnDoNbS+/zoEj5P02+8i/9X/knXvA4h+N206mjBZtIjWOORgkPyCKmS5+rwUFDuQVAISKjx2F/bSCoKODGLCYMyEnoS3jsEYlOkUZ6HKHeCZb3ezeFce039Jp6SgikHRJqJNOkRJRWhSJNGtYolLjiS8azgTJl3NwYMHLsh5P1cEAfT6+nVg/N//ZvHJJ18ya9angMz06WevLncxcwlZMqdfdolidTeB5t6uxGTSI0nV9VP0eh03PPwQBw4cJOD3M65VayRJYsWKX1i+5Cf6X5HC4AEtuaJLPP/37A9kHC4iLMyCy6Nl4vgTPo7ExCSMRgtip64cSc2h8MAyrJ1aYunenpZXDcAYG87Wv7xLyefpCKJI6z4tadE+Cteqzag37Ke/Vc3GLVk8/cJSElLC+enHvXjcfpwOmZlvb6a81E5YDwG1H1xVdqzJEeSs3Y3NGWTk2AmsX/4TBaXpVLo8TOsQT6XPy9afl+MJsWLX+LH9tJjuHSLJyS9HYxS5/87bWLJy3Tk5hJvSkjnxEKr/Xpka/41Go2HSpCk8/fRjDXtwzYxLRmROFxE6XbuSJjmievibaspJlJSUIUnV+3aSkloQFxeL2+0lO/son77xOu78Q/SOCnI0vZg1Kw4SH2lG4/MzLFxHYZWdDbk2Bg++8vi8r0z/DyUGLVqVjKb/IPLnfoNpcBhx4wdTdigb57pdmJFRdW6DIyiRuzeL7O1ZtBD9PDK6LV+vPkyiILN86X5QSQwYMIiUocnkpLmZMO5uYmK2syl3H+2vHsbijZvolV3G4R3ZrLVpCBvVFdXhQxxxBzFEh5FtFGnjzud2Wxlvvfc+pfHh9LmiJcXFHkJSj/L4tR3ZviOHmW/8m4cef+aCl6k4EzXXtb6i5nK5CAQCmEwmZFnm55+X0rp12wY9tsqR9xOU657UKwoaGjNttvlexXpS2w19ol2JG5+v7vVGzjUqVDtnn8dsNhz3E51c6R9O1E/Zv3Ujuqpc7r26F0ePZrAnK4sVqw7xeWY5L1/fmd4pEag1OmasPMrSpYuZPPl65i6cz5JVy4ifMJiYK3uR/tVygg4XR7/+Gdwe4tIy6VtYzMjJQ1lf4iKn+2AcPy1FX5JDp3ZWho/txoCrerB68U7su4twhJpoh8ANQfCoJDbu2sW0++9j1Y2TOLxhI3qzkW9WHqaizIn+peeJ6tIVS+tWeN57H7we5u1OJUWvwmhzM8npp6RQ4tDCnTj9Lt567EpMFj1JapGFB7eyYMF8Jk++vl7Xobq+S9OUXqg5rvoaTmVlpTz33FMEg0ECgSDJySk8/vjTDXpshWXL8Pkr6zxeLYUQYurYoMdwMpeMyPyW82tX0lAm99mLjNcUXzpdvZoawSnMyUMlyGhUoDFqGdurHWWBdLYdLiEyzIwYYiEYhDCDCpfTybpN69ldlk1ouyR6PX83ciBIRJ+O/DjoXobd/ycOfPwJPSMEtGYtBrVEd42KCr2BSoKY9RLBVnH8vCuHmBADH2zOo0Q2QYWd7YZM2oQayQ24seW7mPf1V/yhc3cO/bAUW4gFGzK+66ZgatECBBCMRipt5dx7Sx9iDJ347tstDCtzMlgQ2J2eR5TbyeoYC7LXT8HhQswRFlDb+HH9QiStmgnjJjbQtWhYzvUhFB+fwEcffdHAR9O8ueCO34biZGetRqPGYKjOQ2qu/ZCqI10G/H5/nQpixbfrSLHDx4aDmZg1AmU+GYyR9Onbhb9/s5sHPtzKH2b9yjvLDzJg4CDySouISI5Ho1GjVqkI+v0gV28e04TFkjRwIGFhZgKVdpw5RQgmC+Vr16IrLGDqyEGk7czmQHwkLy86SFnnCeif/Qn9cz+xJ7w1v4oOJt59FV0n9mLL9jWMDA0lISqatr4AHQNQ9f0CPLt348/OJvvttxk9uAPt2rXAbNIRn2Di6/IKns/IZZ1OjbdDK9qGmJm/5AD5lX7WHXGxOccP1jAW/vwDPl/dfWhN65NRdvvWlUvOkmmINIGG3Yz3+9drIl1er7+Wgli1OxL7DbyC8vIy5nz9CbpgJW1bJdOjS2e+WPEpGTY/4ZOuA62W8g9mU1paQmSIFZvBi8ruZe9rcwjt3ob0b1YTGp7EsuXp+HIqKTySyfAW4dgPZ7Jqw36sSW1Iap1MfnElbfUhbJm9htxiL8YxY9FrVciSAXu30ZQV/UC5x4U7KpTYdvGs3H6IKeERmEQVByrKEdRqCr75lqDRyJXxsVw1qgeCGGDxj4uY0DOOvOwy1hc5iYmJwFju4JYIC2/ty2dZVTim9i2x3H0Phen7UR9cgt/vR61W1+l8N21AUKklU1cuGZGR5Wonb80u3vP7ATReWoEoVlsw1bkyZ3bO5eXlsWrlCkRR5Mphw7lm/CSuvnYiSxYtwJ53mJW7MrFVuUm+/wHix1+NX5ZJ1+l5debb/Pz998z64jPGTprA+qW/kPfzbiJNEWj7DoeSYgR3Cf7IJL7Kz2fovSO4qn0Cnzz8OalVyYhhQ1HlrsAY0BIbqcd5aC26Dr0Ier3Y9vyCv6WWwwY9ap0epzvAocICRuq0VIZpiEpI4Aon2K8YTM+evdDpNKzfsBijMUDPlGg27ikg0xLGtW/+BZNGIuvnrcyf9S2CMQR/r4mYe3XClXWQrG27iSzz4na76xUibsotEZerxng8Hl566SU2btyIVqule/fu/Otf/zrt+AsuMg11oXQ6DYIgYLc7mu3FPyEwZw+lZ2Sk8+c7buNqh4OgIPDorPd47ZPPaNEikbHjJlJcXOYXroYAACAASURBVIR7yRLMy1aCKCIDKlEElcj+fXs5eDCNPh27s3/Op3RJasNdf72dhIREDhzYz5IlS1iTKpGbZ6fdhG6MfnIiZTmleGQJ/cOfYgyNIHDV3RT/eSADO4STt/d7Svf+jOx2kSj6iOg5gEMb96CpsNPdL7JGp2ELbq5p1xpDeAQ/HzzCl998xDcHD+DJSOeeKTdQWJhLwCmjt8QR0SUGtyhS6fAgdUzmgM2HOeihRekBjvyYhc0rI8T1QGzZnZfffpe/P/YQRqPprOe3qZdLl2uZh9deew2tVsvSpUsRBIGSkpIzjr/gItMQmEzVT7pgMNggAtOwqQ7VE4miiNmsx+XynrXiXllZGffcNpU/HD3KNZJEZHg40TYb33w0m8f/9jwAkZFRjB49mpmvvYR91geg1+FVaSh75x2ssTF88slHfPvTj+huuhlBpeKHW2/lm9kfYDTq+eajHxjufY1OXj0/f/0kK3v8QvsrOyDojQg6I6IoElRr0VkjCej1TOwfgeR0Up7rI3bANai0ehKr8umU1IHW/aMI0+lZm3oQSdJTXlxBRpSW6E4jsUz7C77yct6/717mffARi7//mn3b17NnVxpDJ12JLtzKgUUbkDQqBvRKpGD/OpxlWsKveYBwnRohNIL8qkzWrFlD585diIuLazZF0S/XnksOh4Pvv/+e1atXH/eBRkREnPE9F7XI1ERnAoEgbrcHk6m55b5U/whPCMzZ9+ocOXKEaY8/SrnPR4vYOLR6HSW5eYSbTNjLKwAoLS3lvy/+g8MH9pPSth37dmwl57/TEbVarCYjTknDxu3bMN55J1G3TAWgJDyMV2a8TYI5ig6uP6DWFhGQHHSz/4GlL8/E5XDhLS4kuHAGnt7jCO5bhdlXwbQXn+X9v7/FwfV78Eka9CWL6Nm1DeV6Aa1OQ5jJSLv4aLb7/cRNHEBrvcSh+YsIJrVD1GjQRETgDfp59vm/EBsdx8a0QoJmNaum/gt9tBVXSSW2Ei9ecyhl5WnEqsPxiCI2tQm9x03qoUMsXP8V28ND0cS35IGnnjmtVdPUm/EuNY3Jz8//Xf0ci8WCxWI5/nd2djahoaG8/fbbbN68GaPRyCOPPELv3r1PO+9FKzIn0gQCxwpIN6QPpWF8Mie6HuhxOs9eOX/ut1/zxLN/xacx4rXZucvrZIrFzBBJxdceD7dedRWBQIBH7rmDwYES/q9NKMvzSjkUYkWNgEOrRzN2HP7t28k8kkmo0UgwEEBUiUiWEEpLSti+ZSuh+go6R7WllziJX8rX4rRXsunjHWg0KlTFm/F+uAC8Hlp2T6AgK59dRwqJ/eFHAiWlaH/5Fm20horsUhb/spItQYkSazjjH3yMtLR9lJUVs2lNBpEj7gbg0LN/JipBQ5sb27Pkb1/SO+Al1mxhUamfxNtvRtCo2fqfOSz9OZOuiS2w2zX4cg9g7jmavEM70exdw8w7hmPRavhk+yF+mPsNN99+53lfm/Pn0rNkpk6dSm5u7imvTZs2jYceOlHVMBAIkJ2dTceOHfnzn//Mrl27uO+++1i+fDkmU+3if1GKTI1v4+R2JQ3dQbIh5lKpqncI1EVgvF4vz/39H2hbdiFCW0qnYT3I3JvHD1kOvj6ax7iBgxh91ViOHs2iKu8oz45qjSAIdLQaWVzkZH2JnaRPP0MTE4P6T38iddJEit5+CzHMiqjWUPr6fyksLUN73WQGSy4Mtgp2bf4QbagdizWFPLUJvyub2B5dSPj0Q3xlZRy4ZiyL5AWo+w9CExGFZ98eWk0ZQ94HHzLZb6RD2ySqMstxhcUQdLm4/bZ7ARg4YCSP/f2vFKslKrMO8+Ta59k2dxPjo/U8GBNBIAg9rQFemP0ZFV41If2ux1d0mKKC7XjaDCElxELZjsWo7E46Rlg4kJNPTno6gtvDwr2ZXDF8JC1aJP7uHDa9T+bSYs6cObVaMicTGxuLJEmMGzcOgG7dumG1Wjly5AhdunSpdd6Lbp+MSiViNhtOKZ1YzYUpNHU6JEl13FdUl94/5eXleIIQLMvglum3MOD+cdw0+2HCI7RYJ06kwG5jzpzPqaiowOH14w5U72z1B4NUeQOIKhWhSckYDUZ8NhvGlGSGD+6B7pNZuF99ibE9e6Nv0xrr6NEE9XpMgwcS0PgIhMUSsOURPmUk1usmUfjhR7jS03GlpxMeGc0Dd92PsH8/kiyDVk/VwVQMfoHOkeGEhoaT0qotvVq0oCIn5/h36devPysW/Mg3b7+LQavBotbgrXCSYlAjqVUYVSKxAR+OtKOoWvQDXznBo1tpmdQKW2UJaYYoyuI6ENqmC2mVdl4q9TFdG8vrOQ4CxYW89ucn6rV/pjG4FPfJxMbGkpCQcMq/34pMWFgY/fr1Y/369UD18r60tJSkpKTTzntRWTIn0gR+bxlciLo0p6PmOB0O93GhOR0+n4/du3dx8OABAo4qVBYJjVkPoogoCliirdgESKsq4ruiXbzzxCx69+nPHzbsYFy0nhUlbpK69kLv83PkP69hGjUK7Qcf0G7DekJ7dqBzQiwRA3vSNroDP/26CU1iIju2hiDuP4zb5mB3TgXSS//FNHQYcjCALz+PI488gsZmZ8bL/6Zv34Fc2bYdK268EU10NOXZaYy5Ziib96XTW2Wke+ce/FpSTEjPXqd8L61WS2JiIh3ik/j40U+I657I7IPFdGgXRgu9mk+OlKNTQVnqz/S9rR9xV49i3UcrMftUxIUk4hd1HPh6HlH9elPUvg2dzFrcrRPptm41aw/vIzU1lU6dOp3ymYpPpml4/vnneeaZZ/j3v/+NJEm8+uqrvxOjk7ngIlPXC6VWqzAYdOeYJtB01Byn3e4mEAiccfu5y+XigccfIsdXRUlFKXE6kcqSMta+s4i2wztzaG0q6Wv3EtBnMeqle2g5ZgDrRYHVXy3F7/GyX9By43W386d77sNut/PMP//OvvvupydBUlrH0ltwkZa2n22HDmMbpqV3u/bsnPYQYs8efLf4Z3q06MLe7A207tQZORgAvxcpNISQtDTenPE/+vXrD8B/X/o3u3fvpqKinHbt2mG329hoWEJaQSGZFeXIrdswZvCQ330/QRB48JEn+PF/b1H5UyoOF9z9ax6S3oAlKpFK2UXXIfGMuGUI81+Zj83ppjSzCKf9O8RAACM+jCEhqFQqWpuM5FkspDrd+Hwups/4D+/MeL/OG/Uamss1ugTQokULPvvsszqPF+QznKniYluDHNTZOFuy7cntSgKB0yfAhYaaqKiwN8gxnctcNflSJx/nmeb5+JOPWJCzg1YPTMRbUU7lD+tQv/UNR3w+DjmNRNMVp1CMQ1/AlF/fRdJp+HrwHfS8tgete7amPLuc/DW5zP3yu+PJgV++8iLGrAza23NI0kJ+IEhqaCTu6BRajJrEkawsso5m0atnT8aNG0fP/v3JtZiJeuQRPEeOUPqv1+joH01l1A5+Wr6Q0NDQ48dbVVXFggXf43A4GDJkCLGxsbhcTjp37ogsc6z+ref4Tmun04HH4+bwgf3kpx7m60WL2Z94BaGJ7fFbIrHtWEE7wxYO/ZpFlRCKaI3Bl7EHCRcj2keRrPGzo8BNYese9OrTjUOffkZXZzlaYKvNR5fhV/PKm2+fFEoNpbzcdsYOAw1FSIgJELDbG6eFcWTkuRexSj36Rr0TJNsmPnrOn3c2LrglczaqW1VoLmg3gbpQm8CcjezCPMJ7tsPmsCOpVMR0a82vRj2ZRVpGql6kV/RNBGQ/X1dO5aepz5AwrA9Op8CO3CR27D5ChKoAR2YRlZWVhIaGIooi1uQUsvfvpp/JjFGnwh8MEm4xUygICILA9ddPweVyoVZLvPTqv8nLz8Wb6SH73nuJCnTkTt9iooLdmJs7lSEjr+R/099i8OAhVFZWMvmGCUR0jSIkLoSZt81AqHJjQEBnsfDeF1/Sp08fzGYDPp+fPXv38t2qZeijrXjLqriyXz96VdpIzXCh6X01WkHAuWc1exfvxBHVEdO0maAPRdy9EudHf2ZHvpO0mCiCgRLsa1axYtNmppj83BplJESlYr3Fx6erfmbz5k30719ThLvp+iBVi3qTfNRFT7MWGb1eg1ot1TlNoMYv05Ab8uoyV42lVdO3qa50adeRNT9+Q1Lb69CEW9j043pyXW5ErYlkcSCiSkQIqkkWB7P68GrKMsrQ3Dkdff+rkJEpevseqDpySm3cIeMn8lF6Gp/t3EBrTQBdiIUNB7OJ7BhO/+QUNm1aR3rGHnbu2sP8TbuInTsXQacj96GHSd7Xn6hAN4LI6KUoHNZQ/njvHTz35LNkZx/FoXLTtlUkA24aSFLfZFb/aRY7gxLfl5fxp5tuZPqsD8jPSkPSaNlblE+/uyZhCQ/DVl7B8s8WcvOUKax/8ily37qDoCgRZs+n99jxfFVmAZ0FUatBtISjU/vwh8Ygp6QQe/dd5D79PNHIhEsqKrQGMlRBKgKQ66lk1jv/Oy4yTdnR8VJ0/DYWzTa6ZDBokaS6C0w1DRlhqttcWq36mMA4613LZPy1ExjZsgerb/oHi8c9SUVaHrJZT2hiCDucnxMMyjgDZRyQ59OlSxfCQkPRJHTAVV6Bfc5/8B84QrglGqfzRMcFk8nEtOf+zoinX2CeR8dTGw/wPXrenv8DP/ywgMLiQ1w7oT95ZcUYxo6h8LXXyHvySbQd2rND/RUFwb3sE+ayT/sxvQfHc+1z43lz1nQ++PQTCltex/Ldcfxr5CuoVCKeYyU5J6rV+B020n9dwqAkLQmqUtKzU6lyudi5ZTO7t2wkvzgXr9fF3M8+oafFj+7AGnQlRzm8bw/igbX4Sorwl5chf/oIYx4by+3v3kmrFInDX3yFKiGOCrORdTaZvBAd2s6t2W+Nxt++PesOHWXu3G+Apu7o2EyiDBcBzdKSOVO7krPRlE+zuizlzmQRiaLIU489ibOqgrnzvsRbXo4ki+gTVBwM/YLd+XPw48EcoWXG9Pm89uYMVix7B39+EXEHtXT3/pV0lnJFv6Gs3bQSqzUMOOaUFAS2F5YQv+wXVGYzrn37ePG22/j6i1dRa9Q4qyoo/fgToh5/HHVCAsWvv443UMGn4kgINdJheHdueeN2ZJ+PdkM68OqY19Fc8yhqrYRbZ+LTx2ZzRSDAexLYA0FkjcSY/p2JjggjwmohfPMONixbRs/kaEKCfory81m7YB7GhCT0Rw8yf0RbtmWVsORoFrlBDfmvTCagUtO2s5kuV3VFExPCgPvHsO2HF5ECKsKSYtiyz015gRuNqwJN3wG0jo7kwA+7+fzb+Vx//Q3Hz3dTcDk7futLsxOZ82lX0pDX/GzLpZNLSpz5x1ZjEdU+ZsGC+ew/uJ53P56K1mxg1oyVrFmRTqeeXSEI48eOZ8KESRgMBl5+/h/c/8gjrN+9jkkcRqM10l6awCx3XwYMHYzRbGXU8GH849lnycvLRd+xI6pjVfD1nToRkGXWrd1BWXkZOoOekGuuwXrTTQBo4uPJunEKE2+dxPfffIc5zIjs84EsYwo3Ewz4EQURn9tHwBBBoMpPZIQRtVFHgd9P77A4Km1OoiPC0KjV9IqL4YtvllJlNaIuryLGEySt1En2tp0MMYqs3ZfDGLVEz1A9j+dX8trkMcxYuoasTCepP26h5dBOBDUavEUlmEPNTBjakVWeUuS+fYgYNBqh3E3GipU4bDbQVm9naPp6Mk3yURc9zUJkZLlmF6/+jFXi6jATTeH8qxaY+i7lamffvl1ERmuZ9/0ewmLDGHXDANZvL6Jbl948/fiTp4w1m8389+WXGLx8BCpRi6hWI8tBVFoNLo0e4f5P+e7bf2L7858Z2L8f5Rs2Yjx0CH3btpTPm4dOI7F/8Q+s+MrJgQIn0riEY2oKst+PwWSi+7UjaBkRy9vvzaDdwDbEd0hg/t++BZUG/9Hd4Pfg+/41EmU3BkMoy0sqELRqwiUV36/fx7hgELvLg10IpUWVh8dMZswRUbxb4GK7mIiYEsusRQd5WeMhLNLMUaeXFkYTGVk55IkBukzoQ0F2Gevu/wC1ToOkUvHgYyPo3C2Bqqwifpj7C/nLNtNNEujv87O7oJx2I646r2twLig9lupOsxAZURQwm09NEzgXGnZDXu2CpddrkSQVNpurQZ6aGZlH2XmkgKF3Xsm+PdksmrsFISqevOLiWsdHRUUT2yKa73Nuo4f/LjLEn7EbChGiU3BvXoivrJDvD61n0aqVGCITOTLlBlRqNVqVQIpJxuv20j/CQIRaZOXSxZTGxqBOTKL83Xe48b4/opIkhg0bwaBBg3nptRfYULmG9m06cFSXi/PNWxC8fnqp3Di8flqWweiWsWSGWPh8zxG6tu9FDnFIoVpGdG+JbddB3LZSVhZVYGt5BYkR8bgSW5GWcYQFO3/hYJkDtV9Fq+RY3tuXytV/m0S7we2w+wJoDVo0R9QUabLRutzMfn0l24pCCPS9FcfWFVh9FQyKsNCjpcwBtx2Px9OkN76yXKo7F1xkqhMI61ZjpY4zNsActQtWjcDY7c56mcqnM60DgQDbd2/jj3PuwRJtpuvEPrz3h5l4Shz0ndDtNHMJzF3wNVcOG85C72YEswpHVSmBQ5VwJBXBZyfunjsJ0UmU/rwSk9SBrr0GkvXLHKLahDNmYjdWLTtAWvYRRrexcHTjWop+XkSL1lEUeCuI2HmIxMm9sVgszP1yPgDbtm1lw7b1pHiq+KSLlTC1lY9Ti/DaPfjdfkyGIJa4KA5nZfCvK184/t2sLVth9MbjO5KDLjoer6SlxOag3eAx7M3aT7w7HItXYFNhLlqzhpjkGMLDwggJBEhsn4xFY2Li+Al8O28W+8olQu56Gnu+jdCWA1n78WP0FSrp0qUrm9JL2bNnNy1axNb9opw3l++O3/pywaNLsgw2m6tBBKa6zm8DHFQtGAw6JEk8tkSqzzGd/v+CwSCBYICktsmIooTPE8AcZqJvYiI333jTad/ndruQVF4i40W8LbqjuecDpITWiH472sgw+iWbuWpoJyZOuwFT0SE2zf8au8vFqzOmMOqqDvzztUm4JQmtpGZ8BwtX94wkOkxNeeY+vn5/NpJ0as2Wzp27IPpFknQq2hk0CLJMC42ErFFxRK3F2b09XkHApDcef49KpWLITTezyWzCY9KxK20nzqM7abHnJ2LXf47OEMHhVrey0toPY9fxdDDHsGTGT1Tkl5Ofms/6j9fQv88ArrrqGjq2H0SJU6Ks3E1klBnRakI2aEm3VfHiguWIRw8x9+Vnee2F55Uav82QCy4yQJO1sag/1YplMOgQRQGbrWF6ZzudTl5/803u+7/HkVQaZk17h/z0IvYu2UXZ/hL+/dIrZyzOtGvXTnr1TcbtF5EGT8Xz8aNEP/hHkr/+Ck2r1hxYvBadyYjZqKNtTCgx7i5wbDNeUJYBmYAsE5HckQMZOVj7teWqv4znTx/cg9qq4Q833sE1wyfwj+f+icvlQqvV8v7M2ez3BNjqC2CQRPYbNez2BDmYX86yBWs5lFnEPXf86ZTjjIuL4+YnnuQP//wXY/u0YXgwnyukcib36kz/MJHKonQ6dxtLXOtRuAQtqvRy3rz+dT667yNG9R+NWq0mNfUQ99xzP2Z7IVLOTiwaH6rDq3DbSlmaW8GURAsT4vSMswbYMu9z1qxZ0yDX6GwoPpm6c8GXSw1JQ7aXrbGKasLp5xLtOjYTJ/t2gsEg9zz8CDt9IQidxlB0xE7Rut0c2vcTsqDC6JWIjIw644wREZFkpBWTnBzGthUfYh41EsvYMRAMEvPKK6ReMYiqQe1xbdiIVt0Ra7uJVOVt4+9/+ZGx4zqyYnkq1tBYrr39YabcNIUHn09m45frcVW5yN6VSwp/pD2D2Jj5Nn/cdycP3D0VWaPhtkef5YlPZhCqE5ANVjyDulGm1yJr1Dw3fMzxwkVOp4NVGzaTXVJOy/hohvbrTXxEGD2HDsSogr1pmcSFBFhzeBnlgRQyDm1F76mkr9uH7JfZGB5CtreIhft/YsemPRQcLGZg926snjcd1yI1epUfo+wgLNRIQqgFIehHdtmJFP0s/2UpV155ZaNbGZdzgmR9uaREpqHR67UEg8HzEJjfk5l5hJ3pR7E+9yMutxt9i564/3cnltv/izahHSWP98Fms50xq7Vfv/5069qP3bs3I2en4df3gGB1vo6/uARBlDi6dCNVlQIuv4Fy/Uo6TrqPstQ1PPPE93g9PkItIeh0egYOGsRbE19jyF3DMFqNiHpo4RpIsjCEOE9fXt0cRq/ocMrUBspUcPPDfyUqMoKUlFaEhVkpKipCo9ESFxd3PH/quyU/U2BKJLx7X9IqCslbuISO8Qns357OqE5tuKK3lV3FFXRtYaP0wKs4PW4Gt0wi1mXB5ddhUrsJG9yT4vxsOj3yJPKizWzdeZg7bprKvj3b2LthDS1irFQIJnZVubm7Two5pZUcycpEzkwjMtKKz+c/Vg7E02iCo4hM3WgWItPYLUjOBZVKJBAI4nC4G/SYZFkmEAhW79IVBPB7kan2DDv3b8Ck12E2nzk5ThAE3nhjJitW/EJa2mFmfvgB+Y8/gaZDByo//hK1EMG2jHyu7N+JuJYhFBXZqJIrWL1rB1/d34dRXWL4fmsOU2+6juFjrmbUw2MZ/7fJIENU62hWP/Q8yc4V+GUXsikES/cRtAqLYsXenSxdv4kpvRLZUZTDyKsnkZLSEoCCggKOHs3CarWSa/eT1Le69EN4VCSHs1NJSEriQEUXPt6xm7LKKuTCKv49ajQrNm/kldWrmJ91FJ8g0KV1R9QJZnyihJjcBYI+dCEW9P0msP/wYh566DEezMggy+EmtlsnNhXuYfu6I/iCQbp1iCZDVlFUVHas86b2eC5VQwrOie6RzVNlpIRJQN1bMkuNLAPNQmQajoZJK6jeEMg5OaNdLhf79u1FEAQ6d+6C2Xwir6isrJS//e1p/IUHKXlhHMKI+wik/YpQcBjfV39DrMzn3en/qdOSTxAERowYyYgRI7n55qk88MD9bJ/xDcPdj7PR/Hemju9Mlw5JqCPj2Ls7l52bt5IUaWZUl+pm7xN7J/Ds/MOUlBYT1S+2+jMFCIkJpUxMY1fwM7ap3qZzq3bERsVRXFSI1ahFG9+eLq2tVNqc7Nm1g34DrmDevG958aW/k5wSSUZ6ET2vupGEIRNRSerq4u4+D1qtjuFjrsF95Qj279+H8ONCDFot81JTuTEY5I5gAKcocmvqfo5UxdBj9350VgslkomKEpEAFURaQykqKkLq0AnHujWk7dhNYpLI1BHdyC2xsWVPGh5LtZvx961+G05wmrs/ZktwB3YcdR5vwkhL8fRFp86XS0pkzteSOVGYPHBOP8Dy8jIeeephCBWQg0Ekh8TsmR+gVmvxeDzcdfetJKcEefGfY1iy7CC/fvsUXr/IB+/OIjo6mpSUFEwmU73r5VgsFj7++BMeffAxFv30HAZ1EJ1JQ9eWakrKcjBUZGDRa9lYXEVxlYtIi56cUif5ZTamXT2Bf776PDHt4tCZdcx/+lvad0ohYFnE6KiueEvKySrIodzmwGHQYg2UYTbGIUkqiipslJSU8MILf2Xmh7fQtl0MmUdKuO3mz4hr2wVrUnuwF9MjIZywsOqUB51OR2RkJLuCQbx+P0crKnlJBr8oohVgdMDPe24/ro8X4dHKVKKhKrYzof5KHnzzvzgcDlwZ6SSKfsaU5ZBuk/hnRgFaoxqrUUNxwU7mf/ctkyZPOX5+Gl5wqsc2V0umuXFJicz58Nu+1Eajrt5zfPjph0T3j2X43aNQiSqWzvyJDz76gPvveZB9+/YiY+Om24ah0pu5t0dbfv31bSKqnOzZs4errhoDQCBQdzP3ZCRJ4u333uIfJX/jhhsnsHZLLloE9DLsTivBGN6OCaMG0+tvK+jfOoINaWX8+elnmTz5eoLBIDMefQu3283YUVfz7F/+ilpdbYWsWfkLC39ZR4WoR1VUTIjFxS9LsijxifS5eiqPPfkI5VV2brtlNuMmdOMvz15NUqKVAdE6IsxOkjq1ISEh8RThbNEikZxhw/lyzSrUBj3r7DZG67So5SBLZC8uv5N9dkjyaXmuZ1d+Cnh49L/vEBERCcCQrj2RUw8y1aghz+3lb6KeiX0SGNkpmrVpZbz7+r8YNnzUKbVwajiz4Hhwu71nFY8TlkxTJmRevFxSInOu0aXa2saei1V08PABokZFUZCZgS8QxJpiJXdrLiCgUqlwOFzIOhOCMQTZ5yeoNZEWlPnkiy954oknzzp/XYiIiOC7eYv4yzNP8OXivYRYQunWaQD3TRhEYlw02yaMZunabYy77yquu+567HYngwcPQeurIjlci8PjZ9XyRVw56hrUajVXjhhF/0GDycvLZer1N1EcSMAQjOageyetd2XhtDj5d8YbSCqR92+ewfT/LKMgv4KBAwditVqxWi04HC7gVOtswNArqejWnfgRo3jugXv5oryMEiAtPJI2//wXkUnJZMyfx2vLljFuxEjCw0/09rnh+utZtGoFTkcVB30ewkJ13DIgEY1Ww21JkSzdV8yyZUu44YbT7zX6f/bOMzCqam3b155eMsmk95CEJLQAIQQIvaOCCIhIU7EgHJSi2D1WjiIqiqIoWAClC0jvvfdA6CkkISG9z0ymz+zvRwRFKQHx/Tger18pa9Zes8u9V3nWc8P1BEd7U8H59R77R2Dqwl0RJ/P/k5v7UtedC6knydt2kki1nEi1nIM/7cZTrUMQoEmTeGxWGV99to39O87y4TtrqPFtAlIFJlP5nftCgF6v5+uvvmPtmp00bdiW9av3MOHNbzmflUvL+Ab069GOqMjIK+VPHt1P24aBtGoaS5ekRviIVWRlZV35v0ql4sSJE+hMzehdtYsuhp8Yat9GyqkUujzTHQ8vDyQKGW1G8BChRwAAIABJREFUdGLlipNM+WAa3t7edWpnu3btWb5jD979B1DiduOf3BZFWAQ2qZTgBwdxqrKS8KRWV708mjdPwBwfz+7AEAgIotjioMruRuGhpsriwAFYrbe2/81ms1NdbaSkpAKz2YpCocDfv1Yk1Wrl715elyd+b+kQ/7P8rXoycGu9jxv7Ut/aHeR0OlE4XcTlVvLR4M9BFFHbReIH1dpEyGQyPvrwM/oNGcy+TC2if1sEYwah9sYYhfO3dKy68tzYiWRss9DdOofC4mP0efxN1s2ZRHpBNR0Tw66Uc1jNeOlCrvzu6aHEYr162d5qtaB2+yIIAtnuneS492CrsXHhQCZNujdDpZKSdyyX/g8MpFevXzcsCoKA2WzGbrej0Wi5Ft7e3nz5+Qz6ZmaSlpWFa/t2BKkMdWgoLomUrvf1uVL25MmT7N23maD4aJaUFGCpsFFmcTJlfTqtYvzJKDVjVXj+Icn4rXCzHk5paSk+PtcPMbgeubkXef/9d6iursbLy4s33nj3mtYufzf+ViJTO1yqW+fsZr7UtzpckslkBIeFc4+/yOuJsVTbHEw8nEP9+vWvlElObkuTsDgupRmRZRyhsaMrvtbG5MTMqPuB6ojT6WTdxlW87KpAgZYI2pNn38m3q/fz9JjxBAYGXinrFxrFyfRMWsVHY7HayCw0ktgw6Kr6OnXqwvuSD/lR6EaORxaStv0QMpqx8Yvt5B7LwW6xUZFZyn3jJ1BVVYVer8fpdDJhwrMs/3kZAgI9evRi8uSpKJXKq+p2u92cPJmKR/1YlGolbpUCQXSTN20q3Vq3u5IsPCcnmz37VnFP7xaknEwhp0CDTRfCEK2aL46cZ0MRKFVqOjdPJCGhxR05j78XHJvNSr9+fQkODqZz5+707z+QoKC67ZmaOvUDHnxwEPfc05tNm9bz8ceTmT595h1p593MXTFc+r/udtbaxmp+SXx95/x7WnTozuNrLtL8mxR6r0ily9AnSEhIuKrMwMEDqCepz+P2xbQRx1DguY3OPdoDtaKmVqtQKP58Bn6JRIJEkGDn1yTmLpmFpLadiPwltuUyiUltcHhFsnzPeTafyCcuqRuBgVeLTEBAAM8+M4xs8RCat1ajGfwm2n+vQeYbRpBDx5hOXTm04DPaRunYsGIRJpOJOXO+40L2ETbseI6NO5+jypDOzJlfXFWvKIrsWLeW03O+w1lSRLNwP4L9pKgFK/rAAHrd35fvFi1j47btpKQcI6KejuKyAiSBHtw/tBMeUUGsvFRMROe2tJjwLxo9PxFjRBR79u390+fw99SKjYSfflrG4MGDOXbsMGvWrKzTZysrK0hPP0+PHrW9vB497iE9/TyVlZV3vJ1/Nd26dePee++lX79+9OvXjz179tyw/N+sJ3Pz3odUKsHDoy6+1LcWc7N9+3YWzlpDoF9DjOZS7DVFrF+5kq49el7Vm+l5Tw8WzFnMtwXtUCpVtGwbz4QX3gV+XT5XKLRIJAJWqx2LxYbTeesrThKJhNGjxrB4zj0kWsZRLD+KWX+Bnj17/aGsTCYjuX1naN/5uvVVVFQQ5e+JXKlE5h8OoojbDaI+GInLSJvmjSgvr2DNkuWsPZjOu/+ZTkR0CKMnJKBWK0CEBx9uwbKFx66qt7i4iBOb1qGqqcJqrqZ9wzDSnW70XZux/OeNHDKpqCy4QPaqpdhzMnmwaxwhzUPwbNmYouw8DCUVRNUPhA6t8Y8Jw+yWUyrEcDoriy6drv99/gyhoaE8+eST9O9/44nlq79nMX5+AVf2pEmlUvz8/CkpKa7T/NXdxvTp04mLi6tT2b+VyNxMGC4LTF1sY291uDRv7nzUUTKiW/pTsTGVx2NVVFjzGPfYEGbMX0pYWDj5+fk8+tCTNDY8RTO1jhThWzp0aYtWq+XkyeN8/8M8TAYDjw4fRocOnVCrlej1tdG/VqsNi8V2S3Yfr73xGpH167F3+1YSw/wZO37tFb/iW12Fk8lkSOVyYiLDyF72PvJeo7GnH8aZdgBnE38WLVlMdXklnobmJNh700N8iLkZ3dmw9hRduzdCFEV270xHpfLGbrejUCgA2L1/N85wFY2jEsjcdYD9P6+DoECMOedAUJOfdgyXM52uY9pQeMyL9csP0c1qw1lmJqvYik2pwtdtprS0kob+AbgrjdQUFeJ9k6jpP8M/+5Zujb+VyNxIGG5FYG6HYkMBnZ7uypFZq5naqh4RKhFLpRmJQWDThnU89fS/WL9+LRHG+2inehaJREI9VzI/fPMYjePjGDLyGZxdRyLYK1n9yGO8POFZxo+fiMlkRi6XoVIp8fHxxO0WrwjOzXavC4LAsGHDGTZsOFDrm2Q0Gm+6beFa6PV6THjw+P2d+fbnNWRt+RadjxePP38fvt4enNp5jkY6OJZRRb3qHuil4cTzMKkpP/Ds04tIyy6lwiLiFRhIn0EDWPDtbHx8fCkxlqCPq8e0BRtJv3AJiQTqRfsjhvgRFBxGzrGtPLfuDUS3nUYtwrBfKCGoWMbG9Awi7u1Cac1F5KZignJzOL18Dbkl1YTY4b6XXr2t61g3bj1hVWBgIGVlJbhcLqRSKS6Xi7KyUgICAm/+4f8jCgsLr+mFfa19dC+++CKiKNKyZUsmTpx4dztI3nn+qDK/tY39q9wn68fEkLpzPwW5BZR7BxDir0GjUeM2WK9kUduwdiOW6saUCeXI5DLQ1aZfeHPyx4hD3kffuhcSiYBJLuGLr2YwaNBQgoODcTicOBxOjMYaFAo5KpUSPz/9lcDB30asLl++lJmzpuOwO3jooaE8++x47HY7T4weya7t20AU6dC5KyuXLq2z+6LT6WTDji0YgnRUyaOJi29Cz36tadWpCXKpG4vDTfoFM4UZ6Yh2L7yF+jhFOwWKA7z33mT27T/AGXc6zWbMALmMi599RptunYiJiiQkOpScqjJqwiMIHvkv7C6B9Lfe5okW3QltE8uX355CdDlw2Cxo3C5kUikhHjp6RNWnS8+H8PDw4OzpVE4f24vtTAYNtN706tX7moF4d4rbiSr39vYhJiaOrVs3cc89vdm6dROxsQ3uqqHS8OHDyc/Pv+pvY8eOZdy4cVf9bcGCBQQHB2O323n//feZNGkSU6dOvW69fyuRuVbSqtsVGFEUkUjqNi8uiiKXzhWSdrAMD1rwdMlpJjSpIiDAnxVlLr7ufT+rV6+i6LSTSsl2AsWmaOz+HDR9zL9eGMw3Cxch8fBGIhEQ7RZQ61CoZIweMZSRzz7HA/36XzmW3V673G4wCLhcTnbt2oHT6aBTp84cOnSYyR+8yXsf3o9Gq2DSmz8il8vJzS9g174DSJMHIHj4s2fnD4x7bjxff/lVnb5fRmY6Br2MZh3aAeAXFcrhFcu4UFCF2WZB7+OLr68fOqWKDcfWclE5nCL7WfxDtMhkMtxSGR49eiBVKak2GND16olx3WqqvGU4pTY0iQ2I+tdoCtbtQhkZj7RnL1q2SKJnz16cOHaANf/+gdYPJXM8JYOM1FyyOzWg79OjiYio3W8TFhaGVqdj/2uvEuJ0MnvnDla1asPnX3x1w7w8t8vtpt586aXXee+9t5kz5zt0Oh1vvvnuHW/bn2HBggXX7Mn8nuDg2tU0hULBsGHDGDNmzA3r/VuJzO/5vS/1X8WePbu5dNTFSMUu5KKWVOdCPj/3Np0iIvhqwRQiI+vx/TdzaOIeQrRvJ/YYp2NwFyF6l/P06FFUm2v46MdXsQdF4zi9G9FpRyETeDaqhslvvoxEIuH+vg9QXFzEuHGjOXz4KD4+XohyHRbvCAQPb8QXX6FjciJP/SuZ1m2jAHjx1a58PX0lmReLkLYZiPqJTwGQxrRi1ZLX+bqO389staLxre0ZlOTlU1pQjN0k4KxRU2a2czrlFNIKBxHBIfyw/Ge+/2Y2JSulBF7sywdj5yEJKcUW5o9zQH/cbhfVW7YiCQ7FrvND8NMiEcFdY8arfjg1VUbMmZlYWidjs1n55MNPmT33OzZN34QgShk7/mUGDBh41QtAFEUmv/IysbYaggP96OUZzMIDe1myZBHDhj1yR6813H5WvHr1Ivn22x/ueHvuFJfF40aYzWZcLhc6nQ5RFFm/fj2NGjW64WfuCpG5U5Nov91WcDu2sbdLXl4uYWIyKpknEiQ0kfXjoPNT+g8ZTmxsHE6nk4ioMA7IDtBa+hSDfL/msHk2lma1kaQvPPccm7ZsJrWqDO3UowiiAN+MIsuWyYeNfZg1fw73932AMWOeIj5BzWezXua9tzew9lIkmme+BUHAseMHjmyZTkT0r7tpy0pN1NSYqTaZkAf+umwtCYi8pVjDQL8AUs8dxlhVzZqFexA1oUiEemSfK+W+ju2RR3cgKCiQ+vVjcLlcrFm1llEcxVMWis1q5dvMZOSui2T3H4DR5USq1RD93UysqamI2anEhIeQu3wl1RVmSrYfJrpBBEU6ke9XLKZ3che2HzhIcWA4pswzvPnhO/ywaC7fzviewMAg3G4338ycQbXajnJ4e6pdsGzLOcL99ew/cugvEpn/3Ynf8vJyxo0bh8vlwu12U79+fd5+++0bfuauEJk7zWXb2D8jMLeyuhQZGUW5xxpKzWl4SyM541yBRO2gZcskLq94DR06jG3rdzEv9V40gg81Ptn8+MH3QO1ys09AELoOAxHUOqSiG3ePkexc/hINtVKkChkOh4NjKceZ+cPrSKUSRJkCaWzrK22QRCYglSv4aWEqVosDjUbOovnH0QWE4j18KBWLP0fWsD2Cpx/Wea/yWP8H6nwuwsLCiC/KZ8KEt9HqW+MdpiCp79NkpixHkMrp3PnXpeJ9+/ZildmZw33Uc3Sks/1dPFTRlAdV4VtRgTm/jNAZM0CjQxrXmLJlywAlCTovykrLSOjTlX7PP41cruDs/qO8/8G75JptWCoK6P+fQQQ1CefYrNWMGjuSFUtWs23rJg4d3MjgiffSuHEol8prMBst7J63l1bN7/xQCe7+VA9/JeHh4axcWbfYoMvcFcF4d4rL/k2Xfan/XA+m7nEy7dq1Z8CYDqzXPcEC4T6Oe03j6/mfXBVVq1Ao+GnVfL5aNok3Zj3Bum2rriR8AogICUbMPIJcIccuitgzjmC323jlbAWPjR6LTCZDq9GQlVlrlZLcKhjn5lm4q0oQ7VbY8DldOrRnzeqN6NRJuGwNmT+vdnLXo3NnAp5/Fsv0IdS83o76CgvTP516S3NOn77/H4LsTp70VxFVfJEtM7+mqNLBmm07rkwW5uZeZPzLz9Hm8/F02PgyVT1zWa4ZRoEylfDJk6nxD6BTuw4YP/0U5+mzOA4ewnbyPOeWbiB13s9onBDXsjlyuYLcs+kYfl5Hm5yLdMrJJthRQ3CTCJRaJYkDWpFbnMeUF55j1czPkdhr8PT15KLRil0lR/TTYvD1Z/+JcxiNxjpe77rzjx3KrSF955133rneP83m2/dAulXqeL/fEKVSjlwuu6FtbF2RSiVIJJI6LXcLgkCbtq0oNOWRWnACQSfDw0NLu+TkX6J3RdRqJSDg7e1LRES9P4TWJyYksPzLyViPb0I8sgr3ic0ktG7NK++8T4cOHREEgaCgEF6eOJ1LeVXs2n4BhSihcvlU7Gs+o0PjaD79cAr+/gF06NCJzp27EhQUhOBysWXqx2j79EablIj70AHmzppFTEz9Kz7el9NmXu/BuXQpj5nTPmZCfW902mCSfT3ZmltEmTGb5IHDyEw7T8N6oWzZspkcH5GEF4cj02vw79aMg19MI+r7r9HGxVKzcwej+vQlXKvl4tKl5G9cj9Js5PGqKh4uKeVEdhZHqypp0aMzaUtXk1xRSZRcjTEjA6XdhjUpBp1awFJSScrSA7zkHYSH2w3uag6mF+EbE4Sp3Mihlcdp2LIJ+XkltGnalLCw8D91L/wetVqJKILD8dfN82m1ypsXug5n3OexU/dIdgUK4iUNb/t4N+NvM1xSKuUoFHJEUfzTAgO3Hoy36KclrDqZS9C76xEkEpbOeQ3VF9N56YWJqFQK5s2bx8GUVKIjwhnx6GOoVFfnq/Hz82PX5k3MmfM9X3w9HafbwoETKTzymwd/4MCHiI2N5ejRI3TrFMC99/ZGEIRfooQV12zXiMceRyaTs+jHeagUCl745nuaN29xZZXKbnf8EoOjx+VyXUlxcHUMjoAI9Pa3s7Z4L6fxorzoEmXGMs7uiyOxQxfKy8vQaLRYiiqQCBLUajWG7EIkCjmCTErpwoW4Tp+m/XtT6NevP3KJwGKdSPCR0/TXqAlxOmlhNNN+7zGO/bCSvIOpdJQpaFS/Ac7CfC5lZLDpjR9o2q0J53edI8jooGV4OM7zRtIcCmrO5bNu0nICKix0DvQn70wGorEcieSvGTL9k+ah7vwtROayL7XJZEGnU/+fH9/hcPD9gkVUJQzGWlmNUqGiPLYL02Z/xM/rN9Ekrj67cyoRWt4P2/azfc9oFsz+Hpns6tMvkUiY9f1MHv58GPH3NSdt1zkeGT6MDas306BBAwCaNWtOs2ZXG7/daJlWEATiYmORSaVUVldz+NhRkpPb/qbtV8fgqNVKPDyuTuIUFhZG0xZJjDmawvBwL3bl52M2Okhyj+HQ3OVYigpIemooBw7vp/jwGTY9/Bo+TWO4tGI37Vq3ofzjqcQGBPDi199w4kQKLpebU8cO09RuIspXSYrZSbHdjd7txuxw4Ko00Ty5AxfOniU/N5MMuwVXvVB6St0Yt59BVmGhZfNE7BIJPiol3QOi2JpXSpDDxL/jQtAqlWQXlJNRbSQoKOi65+Z2+We4dGv814vMb32p71SO31rqngBr7rw5VBoKEVfNxrZ2MaaQSFzh9fBI7oc1vgMrPhtF5PSjSDWeiB0GcXrKAE6eTCUxseVV9eTk5CDVymjaOwFBoaBRrwR8Y0OY8PILbFy19ra+xblzZxn25Aj0r72GPCSEbz7+GIvVyvvv/jFGw253YLfXdrOVSgVqdW2KA7vdwZyFi+nStiOvHnNS7ZLytPs8KvS0do3l6w1NOHFwPe2f6ETX+xpQfTQTe/olIlT+fPbRx3h5+WIwVPPQI8Oo9vBAMFbhayqjqV5BlVoJMoFl1SaWa9XUe7Q356MVrJmznDZB/vSJD6Z5TAI7TmZhwJPwepGMGv4EVlMN3y+YhydSNqecRROqx/OckeLsUmRKOVajlTAPT1Sq2x92XI9/RObWuGtE5nYy0V3Ll/pOTfzfyj20fc82LEVW9GVBSFHizsun8MQWAr7chsQrAFGhxm23ItV4IkilSLSeVx7m31OUX8FH908jrn0sXUZ2obKgkmp7Jc88P56C4mLaJbXiubHjrzs8+j1r1q5B8/DD6AcMAEA6eTLznxrJkIcGUb9+9HU/dznFgSAIKJUKvL31/LD4R3p17Y2fIxEVekScaPFD4lbQclASkRHeNI7y4sQFE1Jzc85U5tGxbQ8eefEpDu/ejzTMj/uTGxJkLiUnq5BGEhep6Zf4othGlUSOV3JT8jfs4eLiDQhaLT6hClok9qLizClim0Xx46FcGiW0pmHDRgiCQHhUJGVlZVxas5qiTYspcLkoN9vxVWuoCNVhrBHx9vap+4WsI9ezHf6Ha/Nfu7qk0fzWl/r/7xXPSr+AUOpJF/EdeoofonF7I7E6kOp8sF86j0J0Ylr3FbaLZ6jeMAtdTQnx8fFX1WE0Gnlk5BN4Dx2G89HxHDhUydut3kIR3xybw8HBwACKHxnOvCOHef6VF+vcNplMBqZf0j2IIk6jETdSTpx2sWr1diyWG3tKiWLtXqnKSgORkTEsWraQMtUJzkuWY6ac/XyCUtDh4eNFRU4Z095excrDaewvP0pEh45ENr6Hbbt2cakgj9Yt6hEZ6IFecCKpLOPDVac5eFpPgGEiKksXSredoV99D6YMjidUsFNdUUHh2TO00HkR4qEjwdcXw5HDZGZmkpGRgVyuwNfXlzOpx2lSY2J020hO+mmZX1XNYpcbd3REnVfQbo3/3SXs2+Gu6cncCr/axpr/0uPUtVdUerGaVrxIJJ0AaM9L5In7KfxkBFpLOXO//Jz123Zw4uc3aBEexqQff7iyG/oyBw8ewBYYSOjLL2Oz2VAnJpLZsSPOo8fxatkSv/HjAdC2bs2a1q35dMrHf1ihuhb9HujP1/36kG8yYd12EJuhBJ3Sh4U/zqFpYnOqatYSERFB45iomzpXiqJIYmJLlqxYxEvjX2VV5tOE0Zp+ilms/GIEcqGaqY396BTgxdycKuZu/J7gzj0p3p+Fn8sB6ecxuC2UlRowNW2J9UgxJi8lB6rn0cv6JtXuHB7tHE5SrD/JsX48OHk7SVEF+AQFsC+jGF+zlPnn9jDjwGZCQn0wVjvRShUYC/Po2zAYrbcXyVoPSow1rBRVhPoG3FXbCv5X+a8TmT9vG1s3am+iuqmMBClmyjCQjwwVZaQhIFDfYWXRqmUEBgZeM4/LbxEEAdHlRBRFlEolEocDhUzGG6++xqcbNvzarlvwDXc4HJzOyWLEG2/x3YtvMMD1I/XpxXHbbHYfmMTJwny6D++LLdKfY+u3c3/bFsTExN707Z+QkMCW3Rt57aU3SFlRjLcrkljzAyi9F/JgqCeCVMLLjXz5JisdL3sWzcKCCPLScSEtj/LcAoLv7cGm79cTMnUqfk26YcnOYMu48fgZvDFZnIguN2E+GirNTs7Yfak5UYq/2UKmzMlZeyWTpz5IWLg3p47n8uNX+8DHmyNWuFQpIOgDyStMx1B0gTdf+KDO5+pW+Ge4dGv8Vw2X6iIwd9JFsq6069iaVH7kGN9wkvns5UMUgpbwkHoEBgaydNlSkrv3JKF9B96dPPmaSajatWuPzmCkZPJkqjZtouS55+nT534GDHgIWV4upVOmUL1xI8VjnuGhhwfXqRdTXl5OgdOJoaKMQElj4uiLFCVJjAG3FDlepKxextGl8zmRWsG8JQfYuHEndnvd4qPeee9N2jwcyiqfAVSG7MMgUSBKpIiiSInVjsXppKawArnTTVNPkQd7JuIrF9i2eh/4B6Bu3hybaEAZGYUkPIBC9xnOZJdzPL2UCd8eplGTeF5570OkSW3YppGyWXATFeNPo6bBaDRyQkP1BARokOh1bMYLU+8HqO7SE0uf/ugaxP9hYv1OUbsg8I/K1BVBvEG/r7T0zkdLXg+p9MbiUOvqKN7UNtbLS4vB8OfnaaRSCVqtCoPh5kOyjIxzdGvfG5wyQECCBE8vLTPnf45MJuHxF95AGPYBLqUO29L/MLJzc54Z/TQ6nedV6RYqKsqZ8ulUsvPyaJPQgjGj/oVMJqO0tIQPp31CfnEx7Vom0b9vP9RqNX5+ftdvFJCWdp6f1i1Fp4Iv/rOI8WShELQYxXy+IJZ+Sa0prCmnSasRyNVBKPXehAZraBQrkpjY/IZ1/x6z2cxjQx5EVphNBy8ZS3KrULglNBIluBJjGdo0AKuPJ/mnzrEyrZxjZU7Cv5mFIigEV0kp+f8azTvPT2TrhlUUXMqjymxG7etNoK8/z44ax/nyNM6dOcX5FZtJTgynXkIo+nrefPPpDoICfchp3gFpt144bXbs+YU0Pp/BF6++/odh6Z0gMNCX6mrLXxqM5+9/+0m3jrpLsVH3Hq8SCUkS/9s+3s34rxCZugoMgKenFpPpz0f8SiQSPDxuLjJarYovp8/g0DQJCfJHOF++mzTHRkp8d7Nj/2Y++/JL5pR5I0segEotx3R0K7avXkWv9UKUOnnvw0kMfGjglfrcbtd1o4yrqqp4dORjpGdm4LLZuf/+vnwy5ZPrzjsc3r+b6txjpJVV8cmXa1Bb9US6O5Au2YCdap7rXI8ytxKH173EBEVj03gQ1aQhalkG3bu1o7CwEKPRgE7nSUhIyDWPcZm9B/ZQrTZQkJtPQW4+RReLaGkRCLXZWHlyP42ah1HjcNDUasNyqZpPsisp9vVBFhaBI+8SOp9QNDh577WXmTbzE/q+9SC7P/mZypPZVBmtJPbsAaWXaHw2jSCVjDyZhOU1DhxOJx6hegrUAfiPeASPEH+qzmXRMC2b2Z989pdM/AYF+VFZWYPT+ddtvP0zIvOK8wjl1N0SxhclH8pa3fbxbsZdMydzvWGOh4cat1vEbL4V4/s70Z29+ZyMRlM7fLM7nCAoyGEX+6xf0MD9IGIJPDpoJG27JWLNzyRMr8FtNWL77m26uN6iRc0ILKp83n1lIPFN468E2xmNBk6dOo1e701sbCxms5nMzAz0ej1Tp3+KtXEQg7Z+QHVGHntHfsi8+T/y+IgnrvMVREL9gzhwLB+XxI511ETOVRYh6N/C/e2zaEL0eOYUUGHOJ8S3BYWCitTj+3A58th38DgOHyn1WrfCfiGTNiXFJN3AAaDSWEm9+EiatantAV3MyKHoSD6hgYEErtSRcek4SZF+WM12zpwvZ47LzZCKKmwSDR73jkXTpD24Xbzy1r9I7NmYo7M38UB6PiP8PTlXVcOz6zfhLZPjKQWNIKEpAocRyZcKuPQ+BAX5U7V6DeUuN+6KKto9PPwvWlm6cmr/oY7cNSLze37rS202112Vr5W46na42dzO5RUuk8nCfb3vZekPYym+YOQe53R0kmBiZfdw8NwUfAZ64z48C4PSieHsYXBYaa77F6LbjdISTIS8I6dPn6JBgwacOXOahx4ZiugfgCn/EtHBIZQX5uGlV1JcVIVTIqX1jBfY2PclKs/l4LLYmDnnG4YPe+TKsCsj4wJ79qZgtdkIClBz9ngGSUmDSSsws3/Z+7h8w5GcS0Wv0LLnyHkS4xoS4+Nm37ktCJ4+CIKGzt2eZt/pU9il5ag8PQlt1JDDmzbROC7uut5Jeg89RXlFePv54Ha7Kb1USuPGTejapTOZJiPrTqo5nH6OjkoNDVo358yOFHQKJR6VpbRd9zWpa7/C0ONRHAgUZhTgyi9koIcKu83EjjMOAAAgAElEQVSBFwI9pBLWCCColAT66akX5kvj0mLOZlcR0KQl6i6d0NhsFG/egSU966qo5jvJP+6Rt85dKTKCIODhob6SXvJu47cCAxARUY+Z8z6lR9v7kchlSJRSlHod8jJfCgsLuK9LW4pL9pFSVohE40GGczUNFANwOIzkOY4SEvIQAKMmjEM1YQKae+7Bp8bKhSHDoMxEw9ggqnMVKM3h7HnqI+oN7ECvNR9hN1nYMeA1Pp32CUOHDMVgMDJ12o9cyC1AqdDg7+OPTisj1CzjnnvH4B+0hbVLvqGHeypSq5wdma+QeF9rOtxzL/d56Tl0+BRafXsEJMi1OlQ+/lQWFRAYFYlErcZmu75BW1KLVmzeuYl9l/bgcroJ0gbSsEFjXC4XJ06fAXUIpxSVWNwmgtWwXa5AYnew2OUkRJBQIxHos+E7hOAg4sJiOJCaxg6HkySnG4dUwQnBhTQxhm1p+aRcKKW02EB8UhCOAjt+sfWJ9dFQZVVgj2uI8sRpGje+fXO3G3FZY/7pydSdu05kbmwbWxduzw/7Oq35w180GuVVAnMZX19f3IKLI+IM2tleJCd/N6nyBSQJg/n2mzlMmDCOwyc3IYuqz8rCF/CyvwcuC607Nrzy1s3LySasYyckogyJxhtdh64o5h3jzD4jo4RUJIKCmbJEYh69l+yfd7Fv3DREl8hXp77Dw7MpW7cu58jZY8h6PIVoKCVj/1ISopuQogwlrF5DLqafp6v7PRKljyMAcoeSw7u/44UXan24My/kUlZVSVBwfZROJ8WXstAHe1CcnYXO4bxhAvKaGhOLlqzi2NHDePv48dGkd5BKpRxLOY5BE4EytiGdew7m1MYVGNIOMvGtd1j8wWQayuVUV5egkUiJcLvIcJnJVjsxeWp5s7SSeFGkyGnD3b4pFS4tNaOexemWUXVwJ4eOb0WmkHN6z1FqPAOQuZzIzmfQplnCH/aF3Wn+iZOpO3eVyNQKjAa73XGbAnOns+zV/myxWDhwYD+i6CQxsSXe3n9c1Tl06BCNVL2R4c3qmlGYxEI87WGsmr2XyuJ/k5lfitej7+NOuBdEF8apQwhBZO782VdEMaZBQ4rXrsVn0HBc1dWYd+zBS4wgQIxHLihxut0EOppxYeEWctYdJOLH+SjiGlC9YgUzv5iEyWBDf/+7yAPisPoXYTaUceLMVtKscmTnT2FNO0RXenJQmMFO2WQcLhN++X5YrVZUKhUtE5uyavV2Uo6cwW01ITNlI2aFozP40bVT5xs+uI+PHsO5rErcLk8qrB6Mfv4FNq1YTl5JKTGJ7TCJEgozLxHkG03z1loGDhzEwq9msKWigr4BARy0WcmyWmkydhD5+44xaNrjhDQIJGvvOVI/30CAjxa5dxs8W3enMu0cirAIJEU67n1+GNtmLuXCV7NQup00CgzijS/rmlj01vn1BfbPMnZduWviZCSSWoGx2ey3LTB/BWazmReffZ0f/72fueNTeaDTo2zZsvkP5dxuN8F+YTSyPYxO1YD20tcYKP2JvpaFZGyzkp6ejr5NH7w8PNCoNGiad2fkiMevxLuIosik117HOvMr0rt1IrNnT+KKOlHGec6711DmzEIQBHytMeQs3ooiriGK2DgQRbwfHIjRVIVKHY4msjOqkBbo/JKQOCWIPiEIr87H+cxkHG27s1F8ji26SWge64/fW69gi4vkPx/WBq3J5XL0UiOx7hO0UGbRsUEIT/TrT/+e9+Dl5XXdc2QymTh14DiamjJiA+To7bm4TAb279+Hn5eOmvIiIsMjaZvQmvq+PnipFRw/nsLQMc8wxcuLJhYzz3t6Ep7cmsAWDam+WECjns3wDPYh8Z6WtOnbhpw9p7FcOE91ejq2Qz8T0yGA2CHdyUvLJLJDU9TAa2OeZf6CxTdd2v9z/DMnc6vcNSKjViuu60t9K/w2z++dYMuWLVhOBROWO4iYosdpnD+eMUNfYtu2rVeVa9MmmVLPQ1yU7cbgysTf3QgRN6IoRVfZHE+dJ+aDq1AolagEF8rMfTRq1PjK58+cPIGyppBN337EqK4tUbsdnHP+RLj2XiLkPZlJIh8TQHbgIkYO7g/ZWVBTg0QiwZqejuiw0yCqCe4ts3AXpOHOOY2YdgRpXDKCXIFErsCVlYJLr8ctyDDlQdmCNdjtbnbs3Q3AudOpxAhmHkxOol+bRBpIbZw+nnLTc5SWdh6NVmD02LbMWTCCJcvGEhXlyZEjR2jTKolwewk5e9eSs2cNFYfW4mPN5cK+pRw/u5/40aPpM/Elvlu2kj69enP2u1V4hAZwbvMJbJVGpKKEsnMlPDPyWeTZmZR9+TxBUaAWSghs04CQPsmUns9myH33MHTIsL9kG8Fv+WdO5ta5a4ZLNTW2O5Id704iCAIWsxlnmSdKSyA6SRAyiQIvdyTjn36RE2lHrqzq+Pr68vl3U+h7/1BqzC7S3Bto7fMuDtFMumkbwf4+GA8voHLfQhyGch4f8jDdunUHaodjpblpdEuMRSKFSf+eQIXRRuHGNjS1D0Et+FApG8t6rxG06NCNNQf3I3eL5Pa9H3XjxpiOHaddYhJJjX3IKlFxZsfXuGxlBPvLKUo/hLu6DMfRtaDzRSy5iPY/O5CExCA6HZjf6oJMV/tgWo0mwn4TvOar86DQWE1NjYlD+3ZSWXwJjU5PUvtuV+1z+nr2bNyCg/YdGwISlCoZHTrXx2n1QKlUMPiBPpSUlJCXd5Fyh57Wsf6cN+uJCwxmxf4MvLv2ZvvhQzw2/FEqqyv5acUylk/8gfC4CASbSM+OvSgrl9Kn96tsOfMN+phgvOMjMV8qoTinkOqiKs4Hl2IwVBAWFoHVasNms/0lQvDPvqVb564RmTs3l3In0z2IJLVK5BPHcwTTGY2gJ1X8ET9ZNDmWHLKyMmnQ4Fc7iJiYWOYvmMmgR0eQqtlIum0PLszI27TEw3aBXRtXcfFiDjqd51X5f0tKSigvLcFUE4ynTodUIiMuLpI9O9fitllwSaopIgupTs3OzGN4vfw8qosXMX86jbHtO9HznfeoLCum+uIp7LZs4lqCTuNFs8TR/LB2L6tfaonLYUfWYSjunJPgG47odCAgIHiH8eA9teH3vsHBZBw8T7CPHkEQSC+pwDexCXu2bSSAMuLjfDDbHOzfuoae/Yai0WgASMvMRtD5sWnrEYYObY/RbGT3zgxeeqE2fkcqlRIcHIzBYMClU+FwOpGpVWg0KnDYUXt6Ul5dyYfTPubchXS6de7K0489hd1ux9PTE7vdxtz5hymuvIhP6waUn8xCEeiLy+4if9cpYgYOpErnzeez5/Kfl19BrVbi6anFZnP8Ijh3bvj9v5xE/Ha5a0TmznFnElfV5uSF2NgGtOwdwYplI/ASw/CXxmKWFSGVu/D0/KNLYcuWSTSuV58TVi/U97+NqJRTfXYFQTVVrFy1kvk/r0KpUDB+5BN06NCRI0eO8dPy/RjKy8g6v4buHRvgGxBETNNWOORLaBh0kUaeSj7LrKKkWkv4N4uRhYWhTW6PNT2dgwcOMnr0GOxh4eypqiLY5wLJjSMJi4rD188Pk1PGBx9+QnZ2DgOGDsPhG4pj3efIe43CdSEFZV4qAwdOASCuYWNMRgPLU2uHSCGNmxMZXZ8dq+fTMN4bmaEGh8mGYJZSUVFxRWTCQ0Moa3Ifc5b+yIo1ZzFVGohv3IzevfvgdotXvK+Dg4NJ2WvHXyvHUFFMWm4psqB6FKSksHv1z5iNFWh1GsokFl586xXmzZqLRqOhsrISh62UoqwTyCIDCb63B9lrNuPYexSxoAxl62QMiJQZqn/J5mdDEARUKiVarRovLw+sVjtWq+26eXzqyu16Lv0d+fLLL/niiy9Ys2YNcXFx1y33txOZO9GTuZwM6/K9NOPrr+h5viclGYWUymqwKcrp1bfzNVM7ut1uinPLaeiI4uL8lxHlKoTicxiahPPah18j6/E4Ep2ekRNfZtbHk9m07SzRDQbh4eHDhbTdvDrtS/KLz2Fzidznp+LrtrVJsJv6enDvwUJcNhtKahNWSWwSdmzfSU2NCa3Wg5at26FwVNEsqTapk9Ppwi2CTCYn80IGUixIq/KxrZ+Bfc00gkLDmT7za8LDw3G73UyZ+hELlixCBB4bOpx+7dpTXFyEqaqcqJBYdFo1QVY7644coNUvcx8ul4seHdqSMu0zRH0glTY7UfVC+eGHRaxevYYP3v4Ys9lMk/gmfPLlFFp2vJcDu7dQXGikyGbAN0KLb7WZsuOpvOWvJ8Ji4Zu8YlL1nqSnp5GQ0AJvb2/u79OKk9uXU73tEtaqGrw27qKfxY5CENi9Yg05QaHUb9/lN/eBiMVixWKxIpFIUKlqs/xJJJIrQnQ7nuj/y55Lv+XMmTOcOHGC0NDQm5b924nMn+WywJhMZjw9tYCAVCpl07ZNzJ79HTk5F4iPT2DIkKHX7DoLgoBWq6W362VKHRe4KGaR4+OP0R6APj4WQRWGXS/H1n0Uy1auRqVsiJdXEBKJhIIyOQXOKOxPTsCWshFd+k+IbjcVdjcV5WZC7Xbyx47Hf+w4HBcvYd2wGw+5DwZDFR4eWjw9PVH5RHD4VAYB3h4UlBvwDW+AzWbjzXf/zQubX+X01pMcXHKAwjMFvD5xAl/Pnc07Uz/CS60m024jfNkycLtZOHEiIUFBdOvSjcjo+mw+fpEIPw0l1RayS0wsWjiP8IhIduzfx+HiQrSDB1H583KkNjN51lKGjxhC0dkaHnAuwMsdzr6jn9O71wDk9fyRab1pGteAYb270rFdW+bOnc39Lhd9dFoEicA7Mie9sy9dtWSe3KY1Dw4dwM41qyjZf5YHLA6aKRXYqqpQ5VvJr6zigUlTrnlN3W43ZrMFs9mCVCpFrVbi5eUBCFcEp+4Wxv/MydjtdiZNmsQnn3zCY489dtPyfzuR+TOrSyqV4jfZ9q7+n1wuZ/ToG3v+Qq3IPP/6s0x7awye9gQcXl54B0ai8omjRBOAqAvBXpSKaDWh0WpRSB2UFGXgcpjIPbMIL72NSpxIuj/Owp0/0D6rjAq7wI4cO3FhsRTn5SG8s5YgZzR625Ok+84lODgEQZAgk7lJSm5LdlY2ZSYDAQ2bEB4eQXp6Omq9mp3f7eDwT/tp/0QX9EHeTHjtVcJefRVtkyakvPACgS+/jOKXuSLPJ59k2eIltGnVGpVXAGEBChAEFi1bTOapszQtOM9PJRZOaH1p9PPPlO/ej++xHYz8ajwB4eF8+dAUAg2dCdRFI1dKSRZHsb9iBgkTH0HhG8CZnYf5aedhIsPD8PLSY5IreKmwmFyFFL3DhUSQ0qhRY0pLS1i3YzuVJhPRUVE0TGhB3upVWM01KD0UNIsM5JDBSv2waMLDI256fVwuFyaTGZPJjExWKzje3p643eIVwbmRX9ffebhUWFh4TS/s3/thf/755zzwwAOEhYXVqd6/ncjcLiqVArlchslkuSIwl4deN7unRFHEZrOhUCiQSCT069+P8Igwfpy/Er1vJFptGE4xhHVbNmJ15WMpP4IsZytPzJuDw+Hko48+w19mJta7BFvT9lQoKzkthOFu1p13Lh3HLYRRPzIOpT4cX8d+qspSKXbuw8c3ELtYRW5uHtHR0YiigEwmITYuFlF0I4rgdkNoaAhVRVUcWXqAp+eNpVGPphxcsJc8IQjdgAGoVSq0iYmYMzIQe/bE5XRhOHsWw6VMHhk5jEH9B6PURJKZfoY9R06R0rcJ/mo5TXPKGWH2wYWA7cxJ2g5pgy7AC5lMSmL/VuzZexypDETRTYHjFBovHQHxTVD7BWK2Wji56TBpaeeRy+Vs08h4ZFBzhidHsed0AbkbcikrK+XLRQuRtW2LR2Agew4ewFhSwhOxcZzNvUie1UJqRhF7g4J5/cOPb/nl4nS6MBrNGI1m5PJa11FfXz1Op+uK4Px+N//feeJ3+PDhV4z6LjN27FjGjRt35ffjx49z+vRpXnzxFlLA3rEW3iVcdpG8FX51PLDc8luqvLycw3s2g8OCW6IgqUNPAgMDSUxsiVbryZGUQkpLq6gxOWkepyW/6AA+3gZemjQbnc6Tnxb/jMloIyZcj2+wL6eOr0Mb2RIhbTm+FTk89ewblFXqkbjCqSyoIKtKS8/7m9O1e3Py886xZ/tmTpw4TnR09C8PwNURqVIpeHp60Dm5K9sPbMW/fhAiIFPKEWtMVxTUf8gQMp54AkVhITVGA+LJo7y15Q2UWiVTe0ym74y5GAQpWrUKf3Wtv1WCnwbn1vNU79iOqFaRcyiVdg+1wu12ofXWYvG8yHxHf3yFGE4LG4i+tzHGmioqZHIcuKgqPMeEV7cilwr4B3vQ4YGmOFzQ56H2nM+0sW/fPhwREUS2qN39re3dh7mzv6ezQkXLyChMDgfpRiM9xo4jKSmJ0tLKW7p2v+WyNYzBcH1rmMu95D/bk3n//Xc4evQwXl61Cwddu3ZnxIin/lSdd4IFCxZcsyfzW44cOcKFCxfo3r02/KKoqIinnnqKDz74gA4dOlyz3rtGZO5cD/TWVpduLDCX67p245xOJ4d3b6J1tA+e2iCyLl5iz6YV9B38BEqlkri4GBRKOdnZhVy4kEl5fire0mpaNm2BVuvBtjWL2bPtBOgjKXY6MZbaaBzbDIXGTf2kGJ4dP4OVq7ahUHgRVi8ejU8J7tOHKSqsICszhfrRTvIvVeHj88eM/IIgXHnriqLAlI8/oG3Hnfz04jyGfv44Ht4aalJOUDplCpqmTalZsoSnRzxBkJ8f0778lP+kfIhXYO1DEJEQSW5uLoF+AXiFBvOf45d4NNqbQyUm1A4H0tmzMRcVkeGy8/2jM/EL9SX/eB5fzprO/h3rwFqN8rw32bmlKM4cw6lQYzxwDJVopN/0x/BQyZj3zGzMLgkqLw8KyyvIv1SKRqPGXVV55Vxv3bEdg9vNMruVoEulRHt64h8a+gcfqj/L9axh8vLy2LBhDV26dMPD48+5IDzyyAgGDhx8J5p7xwgODr5pmVGjRjFq1Kgrv3fr1o2ZM2f+s7p0PW7Wg7lZXWZzDUocGCrL+PiDZVzIM3DJUExOsZEXX3oJQRCIjamPr48nn0z5gOrzCmosJvZuPsr3M7+jS1IkJgLxjOlCYWkqUqWLtEMreeHfrzOifVeUSiXNmsWwZNkxNLp6yOVSmjbRs2fHfDxUAaxbUUF0ZDydOnWmvLyc9Iwc3G6RmPrhV8Xh1FrcBrNnx14GDhnApBavIlfKeW70aNxIyD97jk5PjWLAgAdxu938uOgHLp7Iodk9CZRml5B99AL1RkViMhlp1KIVSw7sYfauXKQaLY2HDEdSWk7PR8ZjFeVk7l5DQw8/ps2bRkREBN27dyUtLYPtG1bRz1XE9iNHUAR6ILWU0XRiH4IbhuAsN2JRezL1i/20bRvJscO5ZOWVk5zcjlOLF5K+ZTOlNiuZW7fSvkcPwkJDyd27jzO7dtGgew8aNWr0l634/NYaprS0nAULFjBlyhSaNm3Gq6++Rb16kX/Ngf9G/O1Epq49mT8zRPq1DjVVNVbem7WM7NMBBNh6oHSdZeYHCygqLGXqpx+xedNm3nz+A2oK5FS5L9BHmIGXEMG2otfZcTgP72BvrKdTsbuMZCuUWPJKadupx5WVlRYJzXDY7ezasx6ZTEtoYDXNWjampNTK0MFDGD78ESorK9m0OQW7S8+aVV9QXp5Nu+S2vPXWpKvscMPCwji09wgeHhokEgnV1YZfHs7a+ZvaoaaEGZ99zZjxo1n11jKqigz0vmcgi9euRdO0CcrE1qiqq4hs2YUGvfpRU1rIthnfEKsLoig1A5+YoWRkHmLv3qMMHhyKXu9JVFQ0UbEN8TareL1pDFv3bSNfJsVYXmvVUpJfgSqhGRXNWrIi9xJhSfGIp+aye/cOnn3scQ4cOsikb2YhiY5GF+iPRXSjbxpPTWYmHbr3QCKR/OWTsaIoEhYWwcqVqzh/Po2NG/+4f+1WWLx4IatW/UxoaBijR48lMjLqDrX0/5bt27fftMzfUGRu3pNRKv+8wAAoFAp8w+I4espEN9s7SN06JGio4AKrf9xBbNxMFn73M/0k33JAXIqIgJdYDx1hREv7cNAyC4xVWLRnsdoFKlK2EB9Xn8WLF3Lq7CmCAoJ5fMQTtGnTipYtW7Bs+TLenrYQz6eewlVWykdffE5ZWSlZWdlkZpZz/vxuHn+6LS0SW/HzT8eY+MI4vprx7VVt1mjUSKVSqquNCILkl4ltAUGoPQ+i6KZFiwS2b9rFkp9WUGUKxO2WclzMR15SQT2tApVPIGUXMgktzidl9XLsbgtb588lJmYoMnkQaq8WZF8ykZWVRWJiAgDJ7TuzcXUJ2aUl5FR5cCnnAqc+XoupyIDT7qRszxEikjrQuE9fynfvJtxuozg3D41Gg81ipaSyEJ1XE4o91PhZ7WSfOomXWkXDhn+dUfy1kEgkNGnShHr1Yq9b5sknh1NcXHTN/61evZlRo57B19cPiUTChg1reeGFcfz006q/fN/V/y/umhy/AHciBYhcLkOhkF03H7BSqUCplGM03jzZuIeHGqvVfsMYipKSEnq2GEQPy+dcZDcibkJIwiQUcsp7BlqNjpG6LXyR3R2zLh2tzBt3dRAOTRABihbopBGctczF7GvBRgmxzho8tFCvYwNKrQJlx4v54bsfMRiMjH7heST/r73zDpCqPPf/Z86Z3md7Zem9KkVEURQrWCB2jSbRxGggzWhyE73RaPyFaMyNxkSvXo3dGEVUUEEsICoKSO+7bGGX7dN7Oef3x+zsLrAsW2Yr8/kLds/MOXtm5jvP+7zP833uuRvj9OlEgyEOXXYxp48wMu30Qpa/sRVRVvHG8p8QwU9Mlrj8wn+wcePW5g5qnU6DRqPB4XBRVlZGIBAgIyODrKx4H5LX68Hj8WC32/H5vHywZhujxy3k4MFv2auPYNHEOLvQQHnJIT56dxXhiaeRc/FC7DWHcX3wJZMyLiHTnE6wphhjrpIpE0LkZFgQNUamzzgDURRxOOwIgkhaWhpVVVW89vor7N+2hcP799MYjqARRMYplVhiEnk3fZei4aN47PFHyf3euUR8YWoO1BOpthPcs5eXnnuZ00+fjkqlxGQyYLe7uviu6TiJ+hqvN3mtCpdeej7PPfcyOTktOZGUx2+/5sTLJY1G1WGB6ShGo5GxM/LZtP6f+GlgIU8RpJFs5Vi8it2UsZrlFf9FxuR6fvXbqzCorPzq7nfQ28/kNG5DISsxqmysLfse6P24HWOJWXSUGxs5/1fns2Lf+yw8by5DbSbKnH6GabXEYhLubzaRZxF4+NHFIEtccMEkvnP5E4RCUVQaDV6/k0gsyopPV3Pe9NmMGD4crVaD0+lmzZrPKDsMkqymtmYdc84YSWZmFp9/cYCKSie1NfWMHjOODRu2cLjahkajobzyUwqmjmV7TMJXUcaIcICqdDPpOhGzUaQxV8uR4i8ZUziNHKuWd7e9w8z8YUwYOop9ZQdYWVnOlVffSEZGiyu+wWDA4HPy8PyZmC+ZzZOv/JuPDzcQNOlxaHQc2FVCuFZBTSBMrqhk1l2X461upHrjbnY+Wsu0aacl5TXsDAqFgk6MvmqT+vq65gbTr7/+CkEQjrovg41BJzInStbGBUadFIGRZZloNEo4HOaddz/hvMuWsEb5Mdu+2kVV4BsKlNPwqaqIiB6mz5nIhyuf5fbzzyRLN4yw4CU3T0t1jQ+PqhKtIh0BCaVeycxHfs6+u1ZwGx/x+p4r2fnRduq3l/DsjELm5Kbx+x1HeO63v6Xgd/fi+fILhhtEJFkCWcZs1iEoBB7904ecNiuXf/97ExPOOAunUuLhx//JuCHjcTpqWbvhYxoafIweNhOjKZO8/DP49/I9KOQNnH3uDwiU7CKnYDIHiz8jI2cGoUg+I0dP4dDBMqref5MZozKZGo3wjVHFMK3IaSMLcWYY8TnsVJetReOyU1rjZXiGmxsuno1Wq6EgO50XV2+hvr6O7OyWVoyKinJkRx1bJB9WvRaVUc/UMTbOmFiAmFbAW5vK2ffpRkaa9FQ98QZSNIpteB67X17LzZdd02wU3pud0fFzdU9lHnrofhyORhQKAYPBwJ/+9FiPO/n1Jf3qL+upwWxdFZi2rmf//gN8tn4bkShEwo1Y02czcdLpTJx0NsvfGsVXK/6PWHQBbkUZimG7GTNhNGvWSuzYXsVl8wQUYT1SRE9l7AtKoquIRj0oBQ0ZU4s49M5XOKhnp28lNvUYvlnzNnIkyoPbq7BvquBXk/LJOHiEwIN/RPIH2eWu54PVB5k4KZ83/vU5GjmNrz8M8tHHHzF25kKmzbuRlx55Gq9rJzvSDuKL5uIzjcQ8ZyFVe7dSqBmFPxDElj4BZ6OJ9Z+9jTc0FKusobKylrHjJ6NQRFEqqhiam0GaJpfFuSZCsoSqaAienGyqP1+Hwmzm0K7dzM9NZ4TNzC5lGrI+ikJQIEkSFdX1OFzuo+79jh07eOihv+LZtI5LlApKiVJqFLnu3OFY1TH2H6kmeriUX84YRebESRzYuYtn/vYm5UVFnFYwjKVLfpast0enSMb7829/+0f3n2QA0a9EJhkc21aQEJh4q0Bnv+2OXnrV1tby8Wf7GDH6MnQ6Ix+tfhl/sJHRoyEmxSgcMpLaWYVs2/YsCxeP4aZbrsMX8LNl83g2rNvDHWWPo1IYaXAV4OMQmu8/gnrYaUR2rOLIWw+jvuKXiNcuZuU7y4jVlmCsiPLYtBxuLLJSE4hy8eflxEQdLzz+BLf99Nf4zvoBj7/yHorGjShMwxGsw0jPziBLM5lZF93AmjfeYurCc1EaCmmsOsSm97/FnHkl+hGziYVUhD0K7I46JuSYOVLVgFKZRV7+GUhoyMk7l4P71jJ33g1MnTaR8r2fcMbpYyiaPBylUkltcQVTZ0TYGogAACAASURBVJ2BVqvD5/NhvuduHA4n0WiMqwqH8Nna93nns2/5x/JP2FrlJhIM8Pirq/ju9ddx+unTWfPxbtwlJTxhKSQzEsTrruK2aIQdboEGvYHDh0rJlSJk2euI7dvFqKF5ZO4spaBoNH9+4MGjXuPe9XhJ9S51lkEnMq1pLTDdHfYG0NjYgN44FJ0ubuw0bvwsPv3kbXy+WZSVVbJnzy7yh5xDo9tKTBFj34EKzJYMyks94ErjiFcgpnNROG0izuBExLGzkWMxxMkXwkfPIo46EzFzKDFTBrFnf4Hgd3Flbi6usA+DUmCiRUPGgutxe4IU5Y+ktnQb8pV/xOw1IezdSIbXSW6Ggi1b17H2zbfQ27QMmVaIo6aSyreq0FSFCfnWEdUVIVvScTRsI6SsxF+gwKhtJCzbUIk1NDbWkJWlR60Q0Io72LFlL5k5LraWHaTeXoleZ0YcNpY5RUNRq+Md4ZIksWXLt7z+n9dBlrjy8kW8tamcrepRZF49j7oP/4V37Fye3FKO+MZvyUubjuz3sk6XjlUFY2WBIaKGVYfDpOFDkvRY7NVcbfEiInNEkhEEJQ/f9/vjqlB7k1QXducZdCKTWOKo1d0XmGOXS3q9nqD/UHO0pNXpmTDOREPNZ+zZtQuzuYisvLMZHTZzsHgDKtVhdu/8HPsuPTfwBkYhjS+Cf6GmYQexQA0Kvwd0RiRPI7LfgzIzH4VGi0KlIipqERQ+1jfamZ1hwB6OsNMv889rr2HfgTrOmHUNO59/AN8/foTGPBWbZSjpBRPZV/whEy7IpOaAl9riKl779Wfo9B7G19zKFdzCId9XbNn2Nn5lI9lpWVjMWRza+zXpNhfDRo1m6PBczJbxlJZ8zcRLF3H2WWdQX1/PmuUvMWHC6ciRIFtLG9AIKlaseJvly9/myJFGUERwWWSGLp6Lc2cJf/ifh9Gq0jFf+FMcG95Cc9XvEPLHEmk4QvRzA7Vb15NhO4vt5lGkRRwsd73BDsmLaWQhoxadzWiTgS8ff5nnd+xHr3FTcsjNTx5YdsLq5t764A+EBsmrhaGdHlPbkww6kUmMRNHp4jmYZEQwCYqKhlJUeIht376DwZCGHKvlhuuuJDMzE5NJy+cbQ9hsWej1c9m1cyMrV5TiqK5jKreiRs/myDOE8FJXvBOFSY3/sesRhk8mVvw1AhHCm1ahENWw5mmG506mqqKEH22qYqxJ4pDPiV/Usn37Tr7+ZjNRxRQunH8XO7avo7GhkpAvwL69n+OL7ENfJaDPVnPBr89FY5jGS3e+AB4NGq2WicJ8aho/p3bIYW5ZfDUjhuQTiUVZ++nzjBquw+FaT2NdlHFjCphz5kxcLhcrVryNpr4CyVRAYWEuDQ4Hy15+iuI6L/ozz8Mw8Uzq//0kFz//MIVTRyLLEh9dfz+V6/Yg7P6CWNCPYMkmGvAiyxKSIR0pHMQ4dB51WhUlTgN+y0QWur4i/VAF37y4Evm7C5h83nTE/YfZ6vZz/YP/xUUXXdLu694bDIQGyf+qDHAk2nGvnDylko9O3sDeZQadyKhUShQKBW63r8sC43DY+WrjViKRGMOG5tLQYGfX7jICwQA+rw+N1kI4UMm11ywgLS2NLd9uxeN2U1a8lvKKciRJANmDoBqNUi1R7fuWOnYxkovIYya+SB1OXyNmsY7LJkzko8b9NAYFIm//A5OliJlTL8Fo1uI84uVCw1s4pf2cm1nIh40/ZOX7/8eESbmseOtRho5cSNDvoXDYYhod+wgZ/Qjpp1O27W1+tuKX5I3Nx+/0Mefms9n24IfMN/2CqBzCqy4mP38IOZkZuH0BREFAqdSRmZnBd75zDgClpaX8v5//jP37DnDYDfPPmMhpE0ezc/cetu3dg23aKMSDbkyZQ5B8fkSzGUGnQ5IlREFAl2nFZNIQ2rUGfwSC/7oH9fnfR9CoiG5cjnnEGEhTk2FNxxr2oFdrmBIJk+nzgiJG8c69+A8cxuDykT3tNBZffc0JX6+OdMoni5THb+cZVCKjVivRatXIstxlgfF4PLz279XoTadjttj4818ew+lSkF8wlvoGN1mZucybNw+ns44XXniDIUV5NNpNaPR5hGKF1B7aSlr6UByNleQXzcVsKeTg168wRD6DAmYRxMUslvKe9GNGjhzFpZf9gMsX3c7LL/+dvLyx5OUNx+XyUHLgQyJyIzqjkTThAg543iOEgwWXzyUU9pM9LIctWz9m/IQfozPkEUrTo8sU8YgHiDYUceibEmy5NqxWGw0H6rAr9/BM4Fyiopcz5k/BnJHNX196nSAmQv561LEdLP3N7YTDYZxOJx/8/QnGVDcwOX0I+8Ie3lv3OXvKDjJjzFi+KXPjmZyLsXobC87LIRCI8G7Ay7Y/vYjw86tx7imj4tNvWXD+hbgdbj74eDMGn5ro8qeJ6L0Yp49DVefC7f6Gau8QFI5DjHXu4AJRpLa6gc2Cgs2NLnB5uOCiS7h32SPtVsPGo4veTPz20qkGCf1KZLqzha1Wx/1APJ6Eo13X2LjxKzZtLsZi9WFLs+ELZmPLOh2dUY2r5AihYCkbvwqAIhOnK4MvNn5JetYkBNFFMGRg1NiLKSgcjiCaUSoVDBk6gsqDo4k1xpAVUQxiGl6hAr1OIje3gcf+/F0WXr4Eg17knHPOa57DlJURoax4G+8duBo9+fjUQUZOPJNdezN5+8M1qK/8MZK8lYoju7DqQ6hyRxNrCBL2l2O8eBHv3P8Xar+twl3vobHEyz3//Sa1tYeIhUv46dJbePiRv1AhV6EeeSaYCvDtruN/nngc89AcnC4nwV3byA/GCHo96F11TBqZSfYMHa++uwZfWCDTWslt35/OrEsmISg1iCEvrz63gfWrvyJLo+b0/EJy0rNY+dlGxsxbinnUaYQanTj9G/Fn+3Dv2I6vcTsxtQFVLMjoiIdKQaAiFGGXRsOMoETd0JH88fEnO1Ru37s5md4512BBvP/+++8/0S/9/t4dsqZQdE1kWgQmgCTJaLXqLg2IKy4u5u//eBeHbzwRaQgb1j1HMKjE4aigsbEaa9ocgqEwleVbGDv5KsKBOjxeieyCReiNhSDkUFf9FXpbIb5IkGDkCKdNm8yoMaP4YtOrxOQgQaGBcstbTLkQrrxqIvbGeg4VH+SMWbPQ6XPRaPWEwyGc9oPcfPPlxMQQFQ3FRNQ2YtEYtU6QJ19GqGgEQs4wZEcDSpeDaCBKgErMk+dQ/+4rTBs5jFsuu4GKvXbOmvtrjOYMxo2bTjgsYDb6ePE/y4lcuhTrnEswjZ5MIBLFU/Il1z/8C3RF2ezctZ3CugZczkYmzBxCYHgWC26YiU4LStI5vO8A55w7nHQdyF4vjmoH8tYqxlt03LLgIuaPG8H/rVyJIn8EeRMuQ6nVoLaYse8voeGbt8g0KzjnrCHMnpROdpqJ4kiMdwNR1pvMeDIz0YwYybMvvoJWqz1pHkStjo+l6a5JeEcwmQwEg5Gk5vrawmDQdPmxL7lceDpRlmwSBG5uZ3hfd+lXkUxXSDiaxQWm65WYsiyzctVaJGEkhUNmEIzE0BnHozdNRIrVkJ55AfW1m4lFIwSCNj5Z83csJjWRaA5Vh3eTn1+ALPlxuaoQTAZEey1Z2R7KGj5nTFo6L7z5OEvvWMIBT4hsk5UjB9J57/UDZOZr+PCNLXy9bjsjx07jwoVXYjZrmHF6ESNHjuDmm9S89cEaRvz8QaRAkLLnXiDs9aFAiXXUJOzbVxMs3ozFK6Lx25Dr95FZk4U2Q8IfEBFVI4hRSOWRKAF/CVqNyL9e+j9KS/cR2f4JPkseNquN8P6NFE0YjqhUotFpyTltLG99vZtchUhlDOYsnITerMdk1GAyK7nnl7/lnfeex3ytBn0gzNrXtlDnD5JuVlO/cxPL6wMgCKisInKoGpWqEG9NDZ5968mWvVx82TTmXnE6kSMNRBqjZFutZE04j5lnno1KpWLYsGFN3dVS0/I3/jr1ZeK1xZ8nFcp0hgEtMiqVEr2++wIjSRLvvvchaz89gMuXg+rI62RljSA9YxyiGCHo9xEMNqJQSEyadg1bNz2N3V6BzjSXmFyNWi0Si5RjMTgwmdwEg6uYc3kRcy67iYPf7mRszEhubi6Fw7NxFsMcaQmjDSPZfmANa7a+SHaugeI6O9+Wl7Lz2WV87+rLueaqcwE4dKgE3dgCgoFy9LZhpM2dyoEVT5M29G6cxZsIfvkf5KALt8ZJWmgyw8TZWMR5lFS9THVNlNlzLmXb9i+oth+iunErKtx4PbV8//Ef8PqDbxGo+pbDdXbGZxmZNCtuCu10OdldUU2tuoD60ekoG/YzyRth05ZSXnnpG6659nY2fPkFO3aUs3dXKbFQmEBIYpxFx59PyyXTaOT64QLnvr+bUGYVtcK7BO1q/KWHuCR6GGO2mUlmLd6qRlSCQHaWiQ3uEkanZxw1VTNOi8Nf3JLieMHprWRsanpk1xiwIpMsgQE4cOAA6784Ql7RVagbJKKSTFnpCpRilDlzr0anyeSzdetQa/OprNhKwO9Eqy8kGHASDXsQpHW46+s4c/Zozjl7IaECM2POOJ2Ax4ujuIIvqiV0Rg9Ol5ICxXS00XR276hGq56EWmNhxJRcDg2dgTDnRiJhPx8eeJ8pq1czccIEKioqCNVUoZ5kI+ytRTVNj/l9H6P3LmfP1q9ZelYerxVn4599Mw2Nduq+eJbs9AwU6SKvvvF3RmQVcfDwNiyzJzNh4mgcznqc3zrYuquBYYvmUvPFZvzbdzNs8gJiB47wWdUbbNyzA1eli7wpC2moXo81s5Bn/udzvC43VqWZFZ99iDB1CDaVyA2NXoaYtTzsDVCUl0ZQo6Ha5Uaj06NXCgxXplFWXkawrpYFjR4WaVWUxgzs3FzBEJ0Si9XEa5/uIihZmXfeBce9Nsc6/MUFRwbkowSnN2gRl5TKdIYBKTIJgfF62xaYznqxut0uAmEzQ4ZOIzPbR2nJXmpjTkYOV5FmKiYmaVDKu/C56hFEK5n5F2Ov+QxRZcFiG0Zl6b+ZNGkOprTRVB3ey0iDnu1vfIhaqUTlAHPeOeTljeQ7Vyt4+8//YogmRHqGEcHkJleVRoWrEfXUxSiM6ciYqTUPY9Watbzyyj+JRv34DpWw+cbrSZ85Cw4d4qbFi9Fo1Fya7eSbw16kcecivPcmWbGhOMIa3MFGLph1FztKXsCx5VOmD8sja2wGdeEIE8z57MtuIDRkOI5DtYRGTEHfGOBreynrn/2GRfMXYDIWMOGqqyn+tJhCp8y4+ihqdRozC3JZ1VhPw+Kz8dkbWKxR8528DESfj7uVIn8+bGfPmBzyowE+21+Lzh/mzIo6PMiIQZkMk5lxooi+1sWWilpeKm5k+LjxTJo4jx/e9mNMpvbtDRKCE39d46+tKEoIgoJYTEr6HPS2zt/0L1JC03EGnMi0Fpj2Rld0hszMTEKBT/F7HWRkFVF3ZCdGkw2NTmTUCBuzZp7OmTPzuO/+pwl49+D1hkAOEPDuo77qIFrDCErLQ1RU7kWgmu9n67nt2htQKBT868UVpKXFfUImTz2XzbPXcODAu0TrlQSl3cy7xsLKj+yEDx9ANXIOWo0Bf9k2Nld+zaQpWcTCEZZ8fyZbvi5jw/p16CdP4WurhZr16ylwVpNlNeJf+zFX6F7FoByGJ1LMqpqrOcLHjD1zOJ5QPdsPe7lLr+Jtn4+0kIjWaKV8cwmFQ9MxjDsNb6ABc1E69gOH+WTDBobOW0jasBHk1tdT/dlhRocjnHXGNEaPHsG7767AlG7DeaQatSShUIqEQlHSkBFsVu7+shTBF2SIBEvHn0l+dgEZ9jr+LoiUBhw8GolgEJSUm9N4/J8vMWVK5/15E4bpCgWIogqtNu6RI4r0aP6mZbmUEpjOMKBEpqMC09FRJgmGDRvOdy6fynP/eoRdkgWX08Go0WO5YMEtlJZ8hdG4n3PmzmHeudtQasayauVbZBZcj6NhM5IUwGSdhjVtCpFQJW6nyJP/fIUrr7gMpVJJNOJm8zermDrtAiLRMFNmTWX0DRp27d7Jex/befu9KurtQaKOVdBQRjTgxuwoRq0WcDQ6ufvW2QzRGzljaDZfHPkC8513UnT22WRfuYj1112D33kYjbcAvSIdWfBi8ItYlIWodDImg4oR54zHv3Ir7674nMr0NBp9IfyOIBrzDBQocG7agXHLTi6u1LLmsJPddV4iX+2k3uFGazBi1yhZp4mhigQoqajAPGUaW9/ZgFBg4+81doRoDJsk8QIgBb1kZ1rQZeZyZaOHbLMNAI0oYioag6XAhDoUQjLoePjWHzFy5MhuvR8EQcBiMeLx+JpecxGFQm4Sg+QnjFuWbd1+qlOKfiUy7b14KpXYiQim/SkDbbFgwcXMmDGNJ5/8J/sPaikqGkEo4GfI0BlUVa0mLc3K4ivn8va7W5gxfQoHSz5Br42hyZlKwF9OVu5sYhEdoUAZ0YiW4uKDrP98C0dq9VTXNrL8rRsIBiViyFgzVIwel8eCB++i7LMNmKL1yFGJTRs2o9PqGXPaJOwOJ05XKe+s2kldgxeFDAqNBkGjwel0Ul1fh2yxsv9QHSqjB0dkE9maSTh0NTgipbDZQ8SkYOH9C9i5o4Iv1+7DmuEkqlZz1rhh7N2xieLNX2O0O3jqsjEYBJHRsTC/qmxEyA5Qu2EN4YAL3eh8wtcs5KVt+zHWOrjg3PNY8+xTBDf5kDNMLFOrkKMyBRqZ15aeh8Gaxm9f+pz3j/hRlO/DmFPEO24Hcl4eN37v+0yYMKHDr0l7KBTxUS9+f7B53GzrCOdE+ZvuiM1AaCnoj/S7OhmhjV6tuMBoO7xEUqtVRKPRTnrHyHy4+mPee38PTk8OpYf2882Xb7B501oqKvZjNqn44ss9uN0eFHID8+cNIxp2I6gL8PkEIqEq3M7tKMUoChqYML6A4nIz06Zfi9MVoby8hqyh1zNuzr14XT7KDn9DIOTgzGnZLLj5Aqw2NVk6gaBLyYMPPobXF+Djzz5j1jkjufm7M9FbdHz7xT58RhsBs5nQ4cPEDh5EpVahH5fGXvvb7NC9zG7lfxCtMaIKmZoaD6te3URlcS3TZg7lptvOIrcwjc8/283cedPZtXEnU2J+Zgkxnt/VwKdyDqXKDJxlh9CGHaiiESYuWYRp2miK135FVUUJn278kmFXn8O0684nlpmGxWpixC2XoPIHyIkGGJJpQW3QUEw61Xo9n9QeQR5SwK9+eAvTp0/vzNuhXcxmI9FolECgbZvVRP5GoUi8oRQIgnzUDlFnRUOpjM/U7o3PRapOphdRKjsnMF1lz549vPTaV2iMF6MV9NRUfUoonE9u5rnoLVF+89unWXz1L5k99xycjkoaalby63tu49nnP8Dp8OP2BfC59yHQyHcWTUerNaA3xOcWHakqwZo5C60xD5XWRO7wK/Hu+orab/YSGqWlobgCjTfKBefNQyVXkpOTw86DB7AV5TFk8lB2ljSATsO4ibl8s241Za+/ji4jE2PIT7oiQuPmUoT8HCb8eBF6i4E9r6zBU17DhNsXo4pE2fe3l/nBz84nzaZn2pl6SvdXs+WL7cjjJ7J79y5eK3dzZMwFBAunIoZljFV7OWvnh0xzu3jv0dcpPXsKaWkqzvre9ez5ZA+jLj+TkCfIaT+ZyoZfPI6AjHXmBIp3bGNmJMr2kjomTZrLXXfd0yOvlcGgA8DnC3To+NaG6a0TxrIsN09p6JjgpPqWAO68804qKysRBAG9Xs99993HuHHjTnh8vxYZpVLEYOi8wHRll+GDD9ciK0aQk38OCkWUw6Xvk5V7PvmFU8jLy6TqyF4OH97HuAlzsVjzKStRo1ZrWXLHIs7ZuZd169chCrnMmHEF5513PqWlh/h0/Wf4fGPR6/W4HVsxpE9GliT8rlKUapGLz7qUw1t3MamwkKF5w/h47Q4mTzoXgCxbGlvDURSFQzBblAgaDe539zD7gR+w9ZFXUdX6+O6lZzAlL4N3P9/CodNGkTYsB01RLlMKc9hy/7Mcfnc9Z9x0MQcApSwTRiTq8RMKBKkJQF6miOXs01i5cQ9RRTqRoIR2eAFDQm4y0zM5y6zFdqSex77aydgHr0Nn0RMLRdDYDATcAfyNboL+IAq1ivK9hynfUMzW/Q0Ihlz+d9lPO3X/O4pWq0GlUuFydd7k/tjlVOsJDR3J36S8ZOIsW7aseSdw7dq1/Pa3v+Xtt98+4fH9VmS6KjBdpaExgE4jEPQfwWAqIBLxIhPFZNajVCqRYkGi0XiY7Pc5kWIetFoter2es8+ew/z589Bq1YiiSCgUZvToMVx5mYOV7z+DTulDrdhK7aEA9ZUZ+P3bmT49jyU/vJXKyko+/XQVO749wJTJc5k3bz4AS398J6+veJNnfv8Gk6cVcPhQPe7MfDI1aqJ1dkwKFaMKslDHYqSZzRxyexGMenQZVlxlNaRNHE6gso6SL3egkBQ8/5fVzL3iNI6U29nyzWGu/MOvmbH4Iurr6nj/kWepbYjQaDIjR6KEakopVErI/igKf5CQJFG/s5Si2eMwaUU2P/E2mnQrNV/vIeLxU/7uBpzfHuDs6eeyePFVXHLJJRiNBmKxGMFgmHA4nJQPZzzxr8Xp9HQ7ojjRSN/2EsapDuw4rUsNvF7vSb/Q+6XItAhMsEsC05VGy/y8HDT6XPbseYcj5X6ioWK8jjW47BZqDlehFYvRKGV273iTUKCGRVeciV6vb358YtKgIAhotWrMZgMXXngec+eeRTAYIhAI8Omnn3DkSBWjRt3KzJmz0Ol0pKWlMXny5OOuZ9iwIv7fb+/jd3/6C+WlB0AVI22qlS13P8lPzdn8u6GO+pp60vKymVqYzn9eW40rLGEbV8SRT79lyKJzKD14mJoN21mYpmVDqYdnX91L3uiJFE6aQbDGjqOyhqDdxVBjOrOzs3j5o7ewez3I9WWoFBF2e3z8R1BgvvFS9q/fhaO8HiSZkk92o0PF1dfdyIzpM4hEQsz94zmkpaUDEA5HsdtdzcZhBoOueaZ0IknbWURRwGQyNFl4JPdLpyMFf/FjYDDXx1RXV7c5C7stJ8Lf/e53fPHFF8iyzLPPPtvu8/aruUsKBWg0cYHx+YLtzjtqD4NBSzgc7fAbWpZlysrKePHlD1BqhhLwNTBiqBJbmp6vvtqOwaDl2muuIC0tDYfDgc1ma9Oh7VhUKmXTIDkVkUiUUCjcoSY+nU6LRqPC5fLy5ptvsuKjtfj9XmZPnoRVq6P+2y18W15BY8TD8Gwz9no7AVeUXV4vtsnDyZ07BcfuQzh3HiK/wY5WqeRIXhEzb/wuQnU13z13HoFQkK0HdmPQ6Fgw70KKiorYu3cPS+/5OW6fA3WjHa1GxWFZicqgx6LVITS6MNnS+eEdS7jwwgswGjs2G0ihUDRZoWoQRYFQKEwwGD7uDd3e461WE35/kFCodzYjEgV/rSds6vU6tFotTmfHckHdoTtzly6oqOiCadUQzjvvPKqqqo763ZIlS1i6dOkJH7tixQpWrVrFM888c8Jj+pXIKJUCFouuWwIDcZGJRKKEwye/0bFYFI/HjSCIhMNhqqqq0GjUFBQUEgqFMJlM3Z7sF7cDVaPVtv6QhdqM0rRaNTpd+0uCQCBe6VxTU8OePbvR6XSMGTMWWZb5y6OPsGPfLvQmIzlGM3l6I8MnTmLGOecQDAbJzc0lP7/ghNfa0NDAihX/4WDxXux2N6NHjeWGG75Lfn5+t+5BgkSkp9GokWUIhUKEQuF2u5otFiORSAy/v+c/3G2REBybzQIocDj8PX7OvhCZzkQyrZk8eTLr1q3DZrO1+ft+JTIKhQK1WtHtHIxer22ai9T+jd64cSNP/OMljlT70OuUzD1rMj/64U2Ulpbxn+WfIstaTIYY37tlMTk5Oe0+V0dp+ZBpkCSpeZklyzJqtQqDQY/L5Un6kqAz9FbkoFQqm2aSq4hGY833ojVGox6FQoHH4+ux6+gI8ShGjcPh65V+qb4QmY7g8/lwu93k5sar2D/55BN+//vfs379+hPmZvpVTkaWZWKxZKx5TzxFMsFHH63hvvufR62fhUarxmA2snlrGaoX/k1dXZixk27FYEzjSNVeXnx5Bff86sdJuK54x7ffH8TvD6JSKdFqNU2iGEOpFHG5vH0sMPHIIRg8/gOfbKLRKF5v/MOgVsfbAwwGHeFwhFAojFIpolSKOJ29+2V3LBqNCp1Og8vl79WGzP5IIBDgZz/7GYFAoKni2sJTTz3VbvK3X4lMsjhZ4tfhsPOnR/5Fet7NmNPOQKEQaTjyGgaNgj27iykoOhuDMZ5zycsfxzdlKwkEAuh0uqReZyQSzxupVCImkxFJkjGbjU3f6G0vp3oak8lINBo7YZFbTxEORwiHI035GzVGox5BEAgGQ4ii0Cf3AuJLeKPRgNcbJBI5xRUGyMjI4I033ujUYwalyMRpW2VkWWb79m0IYg56gw1ZiqA1ZBCTdIRCDnJzswj4qwiHA6jVOhz2KvQ6Aa1W2yNXGd81MeL1+gmHI4iigEajwWw2NS2nQoRCkV7ZOjUa47tlXm/P5xxOhCzLRCJR9HotHo8PpVLZ6l60LC17A4Uivl0bz6F1bVcsxSAVmXgxXts/l2UJtVqN0ahDo/bi8ezGXr+JmsPvcfaZw/jB96+hpKSMjz5+Go0uAylax803XdojfSuCoMBsNuLzBZp3nWIxCb8/gN8faLWc0hEORwmFur4FfDJ0Om2/WJok7klCdMPhSPO90GjU6PXmTu3UdR0Zs9mMJEl4PCFOtvxOcWIGpci0RUJgZFlmzJixjBm1gZLSr4iFYvgd+7nx2qnceecdmExmhgwZwqRJ4/F4PGRmZnR4q7YzKBQKzGYTwWDohLmPxHIqsYTQ63UIgkAoFCIYDCctd6PRqNFq1X0uMBBfrgWDoeMEpOVeXG+QAAAAFklJREFUtOzUGY16QqEIoVCoW7uRbWEwGBBFAYfDR0pguke/2l0CEMXuDzXXaFQIgkAgEAKOFpgE9fX1rP/8a1wuL1OnjOG006Z176SdxGIxEYlEO70tK4pi8xZwLCY1fSC7XlGrUikxmQy4XJ4+y3skMJkMyLLc4eWaIAhoNPF7oVDQnKzurvgmckJOp59otG/uSX/dXeoKg15k2hKYvsZsNiJJUrdzH4mKWpVK2bwj05nllCiKWCxG3G4f0U68KXsCvV7b5Z4kiFeJJwSnO+0MSqWIxWLC6w32aR6mOyKzxuvF34k/XK9QcKHR2OXznYxBuVxqMa3qfwLT2W/r9jh2R8Zg0KFQKJoratv7Rm+d++hrgUmIQ3eWa9FojGg0gM8XaNXOoCcSiXS4nUEQEsu1gZ3ovWutRIWv4+/5IQYFO6/suesZlCKT6C/pbwJjMOgQBAUulzepzyvLMsFgqGm7N76cslpNzd/ox+Z8EvmgQCDYK7OK2iPep6bD5ep+02OCo8VXhV6v60A7g4zJZG6KMFOJ3mQyKEVGkmSUSiVqtSppHcDdRafTolIpu7wc6CixWAyfr+UbXatVNxe4BYNhotEoJpOh+Ru+LxGE+Pa9x+PrkXxQXHzjwtK6cVWW5ebt8EQ7g8FgQBAEnM5UojfZDDqRkSSZSCSM1xs7qoI0GEz+DkRH0Wo1zbs3vSl4rb/RtdqWAjdJiuH19m6x3bEk7DMDgWCPbcu3pnWldaKdwWo1s3LlSsrKyli0aBFGY9opX9HbE7RhdjlwkSQZhUJuEpooHo8Ph8NNLBbDaDRgtZrR6TS96tWqVqvQ6bS4XN4+W7rJskwgEN+FkqQYkUgUq9WM2WxEo1H3yTWZTMZm+4feJt7O4Mdud6FQCLz//vvMnz+fpUvvIBTq2+huMNLvIpmueMEkiAvM0V9FiQ9YIBBCqRTRajXYbOamN3iYSKTnchIqlRKjse8bHoGmRkR1c+6jZTnV+9Fei31m31UWQzz5vWDBpZx//nw2bdrKrl07UKlUfXpNg5F+JzJdRZalk36Qo9EYXq+/uaBLr9ciCPqkF7dBPKEZN1ny9nn9STyaOj65mlhOCUKiX8gA0Nw71RND5bVadbe2qpOHjMlkIhaT8PnCjB8/kfHjJ/bxNQ1OBoXIdERgjj6+xclOFOMO9Far6YR2A51FFAXM5nhCs6/yQAmUShGjUY/bfeLubkk6PtqzWs1Eo7E2q2+7Stw+U5cU+8zuYjTGE72pit6eZ8CLTGcF5lji32SB45YPnXVvSyAIAmazCZ8v0CsJzZNdS6L5sqNil4j2INFukCjfj4tvV0WzJ+0zO0uiYtrp9PWLncfBzoAWme4KzLEcvXzQNG93JmpNTvbtG68/ie+Y9JZNZHvXYrEYCQQCXY5EWvsWazTqpkJCmpeXHY1GEvfF5wv0eeGfSiViMOjxeIJEoymF6Q0GrMgkW2BaE18+BAkEjjaWaq90P7ElGw6H+7z+BGj2pQkGuy92kiQ134/E9q/NZiYajTaV77cvYmazoamRsW+FVxAUmExGAoEwodDAregdaAxIkYkLTO98Cx3bCW0w6Fs147UkRxNmT35/39afxK8lPo6kJ66ltZudRqNGp2tZTrW1vDQa9UiS3Gf+vC3EE73RaAyfL1XR25sMOJGJj6iIGzv39nkTpfvxZrxEcjQuQJIk9/mWLNDcv9QbnrhtjYFpvbzUatX9wqMGUonevqTfiUx7y/y4wEj09eybeDOeH58vviwRRRFRjJtN95VtJnRvumJ3ONa3OB7xxWcre719awAOoNNpUonePqTficyJ6C8C0xq9XotCocDhcDUlRzVYLKZmn5fezEG0VBa7+3R7OBKJIknxyQvBYAitVovBkFhO9b4Aq1Qier0OjyeQSvT2EQNEZOKWDf1JYLRaTasK2qNtM49vTOzZSlqlMlFZ7O21XNWJiO8kGZrtMyHYajllQpalDu/WdZejE719W690KtPvRaY/esLEE57aE9oTHLsVntj6TUQ3yfxb4oV/hqZO5r7/IJnNxuN2nI5fTsV367o7urZ9ZMzmRKK3b3e1TnX6tcj0R4FRqZTN/icn20I/diu8tRF2Mj5cretP+rrwD1p2tdobp3L0bp2qlW9xcsfAGI1GFAoFbndf72oNLhwOB/fccw8VFRWo1WqKior4wx/+0O7Y5n7bhd0fBaY7/UiRSLzz1+FwE4lEMRh02GxmdDotgtC13Y7eGsLWEeJ9YEKHHf8Su1Aulwe3O56oNptNWCwmtFp1tzrl44leFS5XIJXoTTIKhYLbbruN1atX895771FYWMijjz7a7mP6ZSTTHwUmWf1IbbvYdb5PyGzumyFsbaHRqLpln9n2GJjOWWcmSPRHeTyBJE0jjXPVVZehVqtRqzUA3HHHUmbNmp205+8PdGQWttVqZdasWc3/nzp1Kq+99lq7z9vvRMZgUCMICoLBIJFI3+cYoKUfyev1J3VZ0trF7tg+ofZ2YvrDELYEcftMfdLsM7szBiae6DUQCIR6JNH70EPLGD58ZNKft79w4403UlVVddTPlixZwtKlS9s8XpIkXnvtNc4777x2n7ffiUwwGEWvj+9ExGJS05sr1Gdhb0sPUM/64R5f2BafmnjsyJP+MoQNetY+8+iIr6VT/kRjYBSK1onevvUtHqi88sorbUYyJ+LBBx9Er9dz0003tfu8/U5kolEJtzuIIIBWq0Kr1TYVuUUIhXo3ukkMn09EFr3BsTsxrU2lJEnqtqt/suhN+8xjO+UTkxkS5QHBYAibzdKU6PXTUxW9DzxwHyAzadJUbr/9J5hMyR/615fk5uZ2+Nhly5ZRXl7OU089hSC0n9rtt4lfSQK/P4Ld7sPtDjQ77LdYaPb8NZjNxqYBbH2T92htISrLMjpdfB53dxOjySBuRt779pnhcKT5nkSjMTZsWM9FF83n3nt/x9q163rMo/fJJ5/hhRde45lnXgRk/vrXP/fMiQYAjz32GLt27eLJJ59ErT65fWu/FZnWhMMx3O4gdruXUCiCVqslLc2K0WhApRJ75Jwmk6GpH6nvt0ATVgsulxePx4coithsZkwmAypV7wejcftMRZ/2aiWWU2eeOYdly5Zhtzv5xS+WsHHjFz1yvuzsHCDuqLho0dXs3Lm9R87T3zl48CBPP/00dXV1XHfddVxxxRX85Cc/afcx/W651B6J6Mbvj6BWi2i1yqNyF8nK3RiN+qbQO7nzkbpCW0PYjrYQ7Zn52CciPrGyP9hngigqsFjMnH32uZx++hz8fj9arTbp5wkEAk1m9EZkWWbt2tWMHDk66ecZCIwaNYr9+/d36jEDSmRaEw7HCIdjCELoqNxNfJ3e9dxNfBCY2C8+RIklYjB4fNK5fQvREKFQ8pOfiULE/mCfqVCAyWQiEonh98f/Vr1e3yPnstsbuffee5AkiVhMYujQYdx112965FzJ4MFpWnydSJMZelgF+t0s7O6gUgnodCrUalWXopt4EZcmqdMMu4PZbGze5u4oCQtRpVLssoVoWwiCgNVqwuPx9ZvqYlEUmzqrB591Q3dmYd+yNEptfcePz86EF57oOaUZsJFMW0QiEpFICEEIodGo0Ok0raKb9ou6EnUq/UVgjEZ98+iSztDSN5XYCjd2uykxsY3fX9oX4tM4VYNWYAYbg0pkEkgSBAIRAoFIc3RjNhubo5u4o13L8Wq1qlU/Ut8LjF6v7faSra2t8JNZiJ4Ik6l/2GcCqNVK9HotbndyK3pT9ByDUmRak4huFIoQWq3yuOhm06bNjBo1vHm93de0OOknb6naUQvRtohXF/cH+8x4otdoNOL3hwiH+0c1eIqTMyC2sJOBLEMgEMVu9+Ny+ZFl2LTpa37605+wd+9eJKnv37RqdbwruadG2ia2fZ1Od9NWuIDVGt8KV6uPn5yYyO30hpXnyWhJ9EaaE70pBgaDPpJpi0hEoqTkAL/4xS+47bYfMX/+BYii0KHcTU/RkSFsyeTY+Uo6nRajUU8wGG72MW7xzOnxyzkpRqMRAI8nSMqjd2BxSooMgNls4Z57fsdFF12Kw+FHpRLQaltyN4lWgt7I0XRlCFsyab0VHjdIN6FQKPD7g30+iA3iOSqVSsTp9KcSvQOQU1ZkrFYrF1+8oPn/idyN15uou4l/u8cTpSHC4Z6JbpIxhC1ZxGLx+UoaTdyfV6VSotNZmpK+PWsheiLUaiU6XSrRO5A5ZUXmRMRzNy07U1qtCpPJiCTJzR3hyYxu4kPYIkkZwpYMEkPhEv1aR1uIdnyaZjKIj7ZNJXoHOimRaYcTRzdRQqFgt6ObliFsfb9zA4l+LemohtCethA9ES0TOVOJ3oFOSmQ6QE9EN705hK0jxG1AhXZrc47fCo//DR3ZCu8sJpMRWQa3O5XoHeikRKaTtEQ3QbRadXN0E/9m71h001dD2E5EYoRLR2tzkmUheiL0eh1KpZia9jhISIlMF5FlRXN0o1TGq4o7Et30lyFsCRJb512tzemqheiJiN8fDS6XP6neMBUV5fzxj/fjcrmwWCzce+8DFBYOSd4JUpyQU6YYryeJRiU8nhCNjR4CgTBqtZq0NCtmsxG1ukXHj66F6XuBSQw/83r9SWmiDIXCuN3e5i7txPQBjUbdIZOxeKLXgM8XIhJJ7tb5o4/+PxYvvprXX1/O4sVX88gjDyf1+VOcmJTIJJFEdONw+HE4fEiSjMlkJC3Nwo4d21iy5M6mcSp9v1OSSKy2ZSPRXRLJY4fDRSAQRK1WYbNZMBr1KJVtm4wlricUikeHycThsHPgwD7mz78IgPnzL+LAgX04HI6knidF26REpodoHd1s3bqDX/3qLiZPnoxerzsquukrjEZD00iVnrXPbG2XGYvFMJkMrSxUW8KbRKI3XtGbXGpra8nIyEIU4wIniiIZGZnU1dUm/Vwpjqfv3+2DHFlW8L//+7/Mnn0Wt956B7GY1PSBkptK+IO9vnTS63t/Z0uWZQKBEIFACKVSiVarxmYz8/TTT6PV6rjyyiuQJCWpRO/gIyUyvcB99/0BtVqNJIHXG8bnS/jdqJvtFxJjPnr6Q6bRqNFoVH068SAajeL1RlEoQKVS8eyzz/DYY3/h0ksv5667fp10k/Ts7GwaGuqIxWKIokgsFqOhoZ6srOyknidF26SWS72ARnP00kCWFQSD0abcjbcpujGQlmZt8uztGaFpmePdM13enUUUBW699VZWrvyQBx54mOzsnB6ZwmCzpTFy5GjWrl0NwNq1qxk1agw2my3p50pxPIPKfnMgo1DIaDQqtFoVKpUy6dFNf7PPVCjAajUTicTweHp+rEp5eRkPPfR7PB4PJpOJ++57gCFDhvb4ebtKyn4zRdJJRDfBYBSlUtFUVWxAlvVNFbXBLjcIxg3J+499JsiYTCYkSe4164aioqE888wLPX6eU4Fly5axevVqqqqqeO+99xg9uv3JDanlUj8kGpXxesM0Nnrw+eLd0DZbvO5Go1EBnROb+CC2/mGfCWAwxLey3e4AqUTvwOP888/nlVdeIT8/v0PHpyKZfkxb0Y3BYMBg6Hh0YzDE7TP7w5A6oGlJmPyK3hS9x/Tp0zt1/CknMn/84/1s3vwNFosVgHnzzueWW27t46s6OYnopqUjXIVeb232u4lHKUdHBfEeKSUul7tvLvoYlEoBo9GA1xtMekVviu5TXV19XKGo2WzGbDZ363lPOZEBuOmmW/jOd67t68voIieLbuL9QrIs9Sv7zIRHb7w2qD/khVIcy4033khVVdVRP1uyZAlLly7t1vOekiIzWGg7utHy0UdrufvuX7Fq1QdoNMkf29p5ZMxmM5Ik4fWGSOVh+ievvPJKm5FMdzklReb111/lnXeWk59fwO23L2Ho0GF9fUndpCW6KS0t5p577uaWW75Hbm72UdFNX2EwGBBFIWXd0M/Jzc3tkecddHUyP/jBjdTW1rT5u3ffXYPd3kh6egaCIPDBByt59tmneOONd5r7WgY6r776ImVlpfzmN/ei02nQ6eJ1Nwm/m7ZyNz2JVhuf8+R0+olGU3mYjtKdOpk9b0LY2/Hj1UYYf1XHj3/ooYdYs2YNDQ0N2Gw2rFYrq1atOuHxg05kOsull57Pc8+9TE5Oz6h4f0AUFc3LKaDLXi+dRakUsVhMeL3BVB6mk3RHZP5nKLjKO368pQh+Xtbl052UU65Opr6+rvnfX3/9FYIgkJGR2YdX1PPEYjI+X5jGRi9eb7xB0WazNHm9dL7upiMIQsJKIpXoPdU55XIyDz10Pw5HIwqFgMFg4E9/egyl8lS5DQpCoSihULQ5ujEY9BgMhmY3v+RENzImk5lYLJXoTXEKiszf/vaPvr6EfkEiukl0hGu1Kmw2bfMUglCo6+JgMBgQBAGnM5XoTXEKikyKYzk2uol3ahsMCZ/eYKeim4S5utOZquhNESclMimaiUc3EXy+MBpNwljK0hzdhMOhdsfEKpUiBoMerzfYoztJA7Vq+1QlJTJJZPA44isIhWKEQoGjohujsaVn6lgREQRFryZ6B3bV9qnFKbe71JMMRkf8RHTT2OjF4wkgigJWa3xnSqtVo1DIxBO9RqLRRKI3RYoWUiKTJAa/I348unG5gtjtXiKRKHq9jrQ0G1arGUEQetW64fXXX+Xmm6/lv/7rLsrKSnvlnCm6Rmq5lCTac8QfbDaPx+Zu9HoNHk8gaY2YJ6va/tGP7jyqavuuu5YOqqrtwUZKZFJ0g0Tuxp/UZ33uuVfa/X1mZlbzvy+5ZCFPPPFX6uvrBnXV9kAmtVxKEq0d8YGUI34PcipWbQ9kUpFMkmjtiH/RRZemHPF7kIceOpWrtgcep3yDZDIZaI74Kfovg6lBMiX/SSTliJ8ixfGkcjIpUqToUVIikyJFih4ltVwawFx11WWo1WrUag0Ad9yxlFmzZvfxVaVIcTQpkRngPPTQMoYPH9nXl5EixQlJLZdSpEjRo6QimQHOAw/cB8hMmjSV22//CSZT17c+U6ToCVKRzADmySef4YUXXuOZZ14EZP761z/39SWlSHEcKZEZwGRn5wCgVqtZtOhqdu7c3sdXlOJUoLS0lGuvvZaLLrqIa6+9lrKysnaPT4nMACUQCOD1xofryLLM2rWrGTlydB9fVYpTgd///vfccMMNrF69mhtuuIH//u//bvf4VE5mgGK3N3LvvfcgSRKxmMTQocO4667f9PVlpRjkNDY2smfPHp5//nkAFi5cyIMPPojdbictLa3Nx6REZoCSn1/A88+/2teXkaIfYi7o2vHV1dVtzsJuPQ+7urqa7Ozso3yTsrKyqK6u7prIdKdJK0WKFH3DDzZ0/jHBYJArrrgCl8t11M+XLFnC0qVLu3U9qUgmRYoUhMNhli9fftzPW0cxALm5udTW1hKLxRBFkVgsRl1dHbm5JzYMS4lMihQpjlsWnYj09HTGjRvHypUrueKKK1i5ciXjxo074VIJTuInkyJFihTHUlJSwm9+8xvcbjdms5lly5YxfPjwEx6fEpkUKVL0KKk6mRQpUvQoKZFJkSJFj5ISmRQpUvQoKZFJkSJFj5ISmRQpUvQoKZFJkSJFj5ISmRQpUvQoKZFJkSJFj/L/AR3U5g0seJLXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_vis(X, y, dims=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rMgM1fycV1Bf"
   },
   "outputs": [],
   "source": [
    "# visualization of the supervised case\n",
    "# Does our decoder produce the classes and different styles?\n",
    "\n",
    "styles = styles + torch.randn(10, dims).reshape(1, -1).repeat(10, 1).reshape(100, dims)\n",
    "numbers = torch.Tensor(np.eye(10)).repeat(10, 1)\n",
    "coded = torch.cat([numbers, styles], 1).to(device)\n",
    "decoded = decoder(coded).cpu().data.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 339
    },
    "colab_type": "code",
    "id": "fSkNNAQyZ75N",
    "outputId": "7252d2ff-eb55-4cf9-f9df-273444d1fcfa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f7ecd2f01d0>"
      ]
     },
     "execution_count": 297,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(decoded.reshape(10, 10, 28, 28)[0, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EqZxXJCw8ArV"
   },
   "outputs": [],
   "source": [
    "styles = torch.Tensor(\n",
    "    np.concatenate([\n",
    "        np.concatenate([np.linspace(-3., 3., 5).reshape(5, 1), np.zeros(shape=(5, 1))], axis=1),\n",
    "        np.concatenate([np.zeros(shape=(5, 1)), np.linspace(-3., 3., 5).reshape(5, 1)], axis=1),\n",
    "    ], axis=0)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "XcHe6BGlmP2Z",
    "outputId": "e480aeea-8dd0-4875-f39f-0f8d532a149d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x1080 with 100 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "nrows = styles.shape[0]\n",
    "ncols = 10\n",
    "fig, axes = plt.subplots(nrows, ncols, figsize=(15, 15 * nrows / 10))\n",
    "numbers = torch.Tensor(np.eye(10))\n",
    "for row, style in enumerate(styles):\n",
    "    for col, number in enumerate(numbers):\n",
    "        coded = torch.cat([number, style], -1).to(device)\n",
    "        decoded = decoder(coded).cpu().data.numpy()\n",
    "        ax = axes[row, col]\n",
    "        ax.imshow(decoded.reshape(28, 28))\n",
    "        ax.grid(False)\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "\n",
    "fig.tight_layout(pad=-.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "siplushU_xbb"
   },
   "outputs": [],
   "source": []
  }
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